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Research Article Shannon’s Energy Based Algorithm in ECG Signal Processing Hamed Beyramienanlou and Nasser Lotfivand Department of Electronic Engineering, Islamic Azad University, Tabriz Branch, Tabriz, Iran Correspondence should be addressed to Nasser Lotfivand; Lotfi[email protected] Received 20 July 2016; Revised 25 November 2016; Accepted 5 December 2016; Published 18 January 2017 Academic Editor: Luis J. Mena Copyright © 2017 Hamed Beyramienanlou and Nasser Lotfivand. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Physikalisch-Technische Bundesanstalt (PTB) database is electrocardiograms (ECGs) set from healthy volunteers and patients with different heart diseases. PTB is provided for research and teaching purposes by National Metrology Institute of Germany. e analysis method of complex QRS in ECG signals for diagnosis of heart disease is extremely important. In this article, a method on Shannon energy (SE) in order to detect QRS complex in 12 leads of ECG signal is provided. At first, this algorithm computes the Shannon energy (SE) and then makes an envelope of Shannon energy (SE) by using the defined threshold. en, the signal peaks are determined. e efficiency of the algorithm is tested on 70 cases. Of all 12 standard leads, ECG signals include 840 leads of the PTB Diagnostic ECG Database (PTBDB). e algorithm shows that the Shannon energy (SE) sensitivity is equal to 99.924%, the detection error rate (DER) is equal to 0.155%, Positive Predictivity (+P) is equal to 99.922%, and Classification Accuracy (Acc) is equal to 99.846%. 1. Introduction In recent years, cardiovascular disorders have been one of the major diseases threatening human life. erefore, the detection of heart signal waves such as QRS complex is highly significant [1]. Electrocardiogram is used to detect most of heart disorders and shows the electrical activities of heart as a signal [2]. ECG signals contain a lot of information concerning heart diseases. e detection of special points and different parameters such as QRS complex are one of the basic topics and are of high importance, because they lead to the diagnosis of heart diseases. e QRS are used to diagnose many cardiac diseases and noncardiac pathologies such as autonomic malfunction vascular, respiratory (RR) assessment in cardiomyopathy and the normal ventricular myocardium, estimate the heart rate and heart rate variability analysis, and detect ST segment [3–5]. Heart problems usu- ally involve leaking valves and blocked coronary arteries. is research is motivated by reasons expressed. Heart rate cycle consists of a P-wave, a QRS complex, T-wave, and sometimes U-wave [5]. Figure 1 shows schematic representation of normal ECG. Detecting any of heart signal waves may be difficult due to variable physiology, arrhythmia, disease, and noise. erefore, in methods such as artificial neural networks and supportive vector machines, detection by the wave R is not always successful and true detection cannot be reached in different signals [6, 7]. e shape of the waves T, P, and QRS is well known; however, the time and frequency of these waves depend on the physiological and physical conditions. In addition, the signal may face polluted recordings with noises such as transmission lines [3]. In recent decades, various methods have been presented to improve the detection of heart signal waves, including Pan- Tompkins algorithm [7], Wavelet Transform, by usage of a constant scale in signal analysis, not considering the charac- teristics of the signal [8, 9], and artificial neural networks, containing of a series of interconnected simple processing units that each connection has a weight. Input layer, one or multiple hidden layers, and output layer constitute a neural network [10, 11]. Adaptive filter [12], called Hilbert-Huang Transform (HHT), is a new technique for extracting features that are nonlinear and nonstationary signals. is technique Hindawi Computational and Mathematical Methods in Medicine Volume 2017, Article ID 8081361, 16 pages https://doi.org/10.1155/2017/8081361

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Page 1: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

Research ArticleShannonrsquos Energy Based Algorithm in ECG Signal Processing

Hamed Beyramienanlou and Nasser Lotfivand

Department of Electronic Engineering Islamic Azad University Tabriz Branch Tabriz Iran

Correspondence should be addressed to Nasser Lotfivand LotfivandIautacir

Received 20 July 2016 Revised 25 November 2016 Accepted 5 December 2016 Published 18 January 2017

Academic Editor Luis J Mena

Copyright copy 2017 Hamed Beyramienanlou and Nasser Lotfivand This is an open access article distributed under the CreativeCommons Attribution License which permits unrestricted use distribution and reproduction in any medium provided theoriginal work is properly cited

Physikalisch-Technische Bundesanstalt (PTB) database is electrocardiograms (ECGs) set from healthy volunteers and patients withdifferent heart diseases PTB is provided for research and teaching purposes by National Metrology Institute of Germany Theanalysis method of complex QRS in ECG signals for diagnosis of heart disease is extremely important In this article a method onShannon energy (SE) in order to detect QRS complex in 12 leads of ECG signal is provided At first this algorithm computes theShannon energy (SE) and then makes an envelope of Shannon energy (SE) by using the defined threshold Then the signal peaksare determined The efficiency of the algorithm is tested on 70 cases Of all 12 standard leads ECG signals include 840 leads of thePTB Diagnostic ECG Database (PTBDB) The algorithm shows that the Shannon energy (SE) sensitivity is equal to 99924 thedetection error rate (DER) is equal to 0155 Positive Predictivity (+P) is equal to 99922 and Classification Accuracy (Acc) isequal to 99846

1 Introduction

In recent years cardiovascular disorders have been one ofthe major diseases threatening human life Therefore thedetection of heart signal waves such as QRS complex is highlysignificant [1] Electrocardiogram is used to detect most ofheart disorders and shows the electrical activities of heartas a signal [2] ECG signals contain a lot of informationconcerning heart diseases The detection of special pointsand different parameters such as QRS complex are one ofthe basic topics and are of high importance because theylead to the diagnosis of heart diseases The QRS are used todiagnose many cardiac diseases and noncardiac pathologiessuch as autonomic malfunction vascular respiratory (RR)assessment in cardiomyopathy and the normal ventricularmyocardium estimate the heart rate and heart rate variabilityanalysis and detect ST segment [3ndash5] Heart problems usu-ally involve leaking valves and blocked coronary arteriesThisresearch is motivated by reasons expressed Heart rate cycleconsists of a P-wave a QRS complex T-wave and sometimesU-wave [5] Figure 1 shows schematic representation ofnormal ECG

Detecting any of heart signal waves may be difficultdue to variable physiology arrhythmia disease and noiseTherefore in methods such as artificial neural networks andsupportive vector machines detection by the wave R is notalways successful and true detection cannot be reached indifferent signals [6 7]

The shape of the waves T P and QRS is well knownhowever the time and frequency of these waves dependon the physiological and physical conditions In additionthe signal may face polluted recordings with noises such astransmission lines [3]

In recent decades various methods have been presentedto improve the detection of heart signal waves including Pan-Tompkins algorithm [7] Wavelet Transform by usage of aconstant scale in signal analysis not considering the charac-teristics of the signal [8 9] and artificial neural networkscontaining of a series of interconnected simple processingunits that each connection has a weight Input layer one ormultiple hidden layers and output layer constitute a neuralnetwork [10 11] Adaptive filter [12] called Hilbert-HuangTransform (HHT) is a new technique for extracting featuresthat are nonlinear and nonstationary signals This technique

HindawiComputational and Mathematical Methods in MedicineVolume 2017 Article ID 8081361 16 pageshttpsdoiorg10115520178081361

2 Computational and Mathematical Methods in Medicine

PRsegment

STsegment

PT

RPinterval

QTinterval

QS

QRScomplex

Figure 1 Schematic diagram of normal ECG

has a leakage in practical tasks [13] Filter bank [14] a HiddenMarkov Model (HMM) describes the process where directobservation is not possible when sequence of symbols canobserve HMM It is used in many fields such as classificationof heartbeat and apnea bradycardia detection in preterminfants [15] Hermite Transform (HT) was recently usedinstead of Fourier Transform HT shows better performancewhen optimization is done properly [16] Threshold method[17] Shannon energy envelope (SEE) is the average spectrumof energy and is better able to detect peaks in case of variousQRS polarities and sudden changes in QRS amplitude SEEdetects R-peak with a better estimate [18] S-Transform andShannon energy (SSE) create a frequency-dependent regula-tion which is directly related with the Fourier spectrum S-Transform includes short time Fourier Transform (STFT) andtheWavelet Transform (WT) SSE gives a smooth cover for P-waves and T-waves and completely decreases their influence[19] Methods such as pattern matching are based on theircomparing and contrastingThe calculations are complex andneed manual classification [6]

In this paper an algorithm based on Shannon energyhas been proposed to improve the QRS complex detectionand simplify the detection process First a band-pass filter isused for eliminating noise Second Shannon energy of ECGsignal is calculated Third include moving averages and adifferential for the envelope of step 2 Finally with defininga threshold peaks are detected The proposed algorithm istested on 115-second (to end) ECG signal of PTB DiagnosticECG Database (PTBDB) [20 21] and detection accuracy of99846 is obtainedThe proposed technique results in goodperformance without being mathematically complex

2 Method

The block diagram of detecting QRS complex algorithm isshown in Figure 2 It includes four stages Stage 1 includesband-pass digital filter and amplitude normalization Stage

2 includes calculating Shannon energy of stage 1 In stage3 with moving average and differencing make a pack ofShannon energy and in stage 4 with defining a thresholdQRS complex is detected

21 Preparations Signal Digital-analog conversion processis causing all kinds of noise interference and sometimesstrongly affects the information These interactions includefrequency interference muscle contraction and wanderingsignals from the baseline or Gaussian white noise [5]

The ECG signal recorded from human beings is a poorsignal and is often contaminated by noise Frequency interfer-ence includes a narrow band from 48 to 60Hz and harmonicinterference and the noise frommuscle contraction occurs in38 to 45Hz To eliminate this noise notch filter is good [22]Deep breathing loosely connected electrodes and suddenchanges in voltage lead the baseline signal to be wondered(baseline drift) [5] Random variable vector (mean) and chro-matogram baseline estimation and denoising using sparsity(BEADS) algorithm [23] are good methods to eliminatebaseline drift The band-pass filter decreases efficacy ofmuscle contraction frequency interference baseline driftand P-wave and T-wave interference [7 24] To repress thesenoises Butterworth band-pass digital filter with stop-pointset at 5 to 16Hz is used Butterworth has no ripple in band-pass [25] After band-pass filter the signal is normalizedwith(1) in stage 1 [26]

119886 [119899] =1003816100381610038161003816119891 [119899]1003816100381610038161003816

max119873119894=1

1003816100381610038161003816119891 [119899]1003816100381610038161003816 (1)

where 119886[119899] is a normalized amplitude 119891[119899] is an afterprocesses band-pass filter (BPF) 119873 denotes the number ofsamples

22 Shannon Energy and Detection of QRS Complex Theproposed method is based on the use of signal energy The

Computational and Mathematical Methods in Medicine 3

Band-pass filter(BPF)

Amplitudenormalization

(AN)

Stage 2Shannon energy

(SE)

Moving average(MA)

Amplitudenormalization

(AN)Envelope

Stage 4 QRS complex detection in ECGsignal

Stage 1

Stage 3

s[n] s[n]

s[n]

f[n]ECG x[n] f[n]

Figure 2 Block diagram to detect QRS complex

signal square is very close to the signal energy For discretetime signal energy is defined as follows

119864119909=infin

summinusinfin

119909 (119899) 119909lowast (119899) =infin

summinusinfin

|119909 (119899)|2 (2)

Here 119864119909expresses the signal energy 119909(119899) defined ECG

data and 119899 is samplessum represents sum from (minusinfin infin) [27]To explain we have the following

119864119909= (11990920+ 11990921+ 11990922+ 11990923+ sdot sdot sdot) (3)

Shannon energy calculates the average spectrum of thesignal energy In other words discount the high componentsinto the low components So input amplitude is not impor-tant Shannon energy andHilbert Transform (SEHT) providea good accessory for detecting R-peak but this techniquehas a problem SEHT needs high memory and has delays[28] It is designed for solving our actual requirements Tofind smooth Shannon energy zero-phase filter and Shannonenergy approximate are playing a basic role [24 28]

Shannon energy (SE) calculates the energy of the localspectrum for each sample Below is a calculation of Shannonenergy

SE = minus |119886 [119899]| log (|119886 [119899]|)

119904 [119899] = minus1198862 [119899] log (1198862 [119899]) (4)

where 119886[119899] is after process normalizationEnergy that better approaches detection ranges in pres-

ence of noise or domains with more width results in fewererrors Capacity to emphasize medium is the advantage ofusing Shannon energy rather than classic energy [18 19] Theselected signal is normalized with (5) in stage 3 for decreasingthe signal base and placing the signal below the baseline

119904 [119899] = 119904 [119899] minus 120583120590 (5)

where 120583 is the random variable vector and 120590 defined standarddeviation of the signal

In stage 3 after computing Shannon energy small spikesaround the main peak of the energy are generated Thesespikes make main peaks detection difficult To eliminatethis spike Shannon energy is converted into energy package(Shannon energy envelope (SEE)) To overcome this problemthe Hilbert Transform is used SEHT method is a simple andhigh accessory but the SEHT needs high memory and hasdelays so it is unfit for real time detection [24 28] To smoothout the spikes rectangular (ℎ) with 119871 length is used Filteringoperation is shown as follows

119898[119899] = filter (ℎ 119895 119878)

1198981015840 [119899] = filter (ℎ 119895 119878) (6)

where 119898[119899] defines moving average 119895 is a constant and119878 defines Shannon energy from previous steps For spikesreducing and enveloping the nonzero peaks obtained fromdifferential get linked In other words diagnosed peaks arelinked together

Difference is defined below

119889 [119899] = 119891 [119899] minus 119891 [119899 minus 1] 119899 = 2 3 (7)

The sign is defined as follows

sgn (119909)

minus1 if 119909 lt 00 if 119909 = 01 if 119909 gt 0

(8)

where 119909 is a real numberIn stage 4 positive peaks are QRS complex location To

detect QRS complex a threshold (see (9)) is defined In

4 Computational and Mathematical Methods in Medicine

(a) (b)

Figure 3 Simulation result Time and number of peaks detection in each lead are shown ((a) record s0292lrem (b) record s0291lrem)

Sample

005

48000460004400042000minus1

minus05

Am

plitu

def[n

]

(a)

Sample

31

48000460004400042000minus1

Am

plitu

des[n]

(b)

Sample

20

48000460004400042000minus2

Am

plitu

dee[n]

(c)

Am

plitu

de

Sample

0

48000460004400042000minus2

(d)

Figure 4 Process of preparations of ECG signal (record s0291lrem lead v3)

fact samples with greater amplitude than the threshold areselected as output

threshold = 10038161003816100381610038161003816120581120583 (1 minus 1205902)10038161003816100381610038161003816 if 120590 lt 120583

threshold = 10038161003816100381610038161003816120581120590 (1 minus 1205832)10038161003816100381610038161003816 if 120590 gt 120583(9)

where 120581 is a constant

3 Result

The experimental results are obtained after simulation on 70healthy patientsrsquo signals for all 12 leads and using PTB Diag-nostic ECGDatabase (PTBDB)The Physikalisch-TechnischeBundesanstalt (PTB) is the National Metrology Institute ofGermany PTB database is provided for PhysioNet and hasdifferent morphologies The ECGs in this database obtain 15input channels including the conventional 12 leads (i ii iiiavr avl avf v1 v2 v3 v4 v5 and v6) together with the 3

Frank lead ECGs (vx vy and vz) Input voltage is plusmn16mVinput resistance is 100Ω ADC resolution is equal to 16 bitswith 05120583LSB and sampling frequency is equal to 1 KHz[20 21]The proposed algorithmwas performed on a 24GHzIntel core i3 CPU using GNU Octave version 402 [29] Aselected signal from patient 117 has a variety of physiologyand baseline drift Leads (i ii avl avf v3 v4 v5 and v6) ofrecord s0291lrem and leads (i ii iii avf v1 v2 v4 v5 and v6)of record s0292lrem have high amplitude Leads (i avl v2 v3and v4) of record s0291lrem and leads (avr avl and avf) ofrecord s0292lrem have a sharp and tall T-wave

Figure 3 shows the result of simulation to detect each leadof patient 117 in Octave Figures 4 and 5 show the process ofECG signal provision and peak detectionThe QRS detectionof the 12 channels of healthy ECG signal in patient 117 ofthe PTB database is reported in Table 1 and the AppendixDetection of the 12 leads is shown in Figure 6 Figure 7 shows3 leads of 3 cases

Computational and Mathematical Methods in Medicine 5

Table 1 The QRS detection of ECG signal of the PTB database

Case TP FN FP DER Se +P Accs0010 rem 624 0 0 0000 100000 100000 100000s0014lrem 1987 0 0 0000 100000 100000 100000s0015lrem 1815 0 2 0110 100000 99890 99890s0017lrem 1673 0 5 0299 100000 99702 99702s0020arem 1906 4 21 1312 99791 98910 98705s0020brem 1867 5 20 1339 99733 98940 98679s0021arem 2207 1 0 0045 99955 100000 99955s0021brem 2196 0 0 0000 100000 100000 100000s0025lrem 2382 6 0 0252 99749 100000 99749s0029lrem 1638 0 0 0000 100000 100000 100000s0031lrem 2111 1 0 0047 99953 100000 99953s0035 rem 552 0 0 0000 100000 100000 100000s0036lrem 2066 0 2 0097 100000 99903 99903s0037lrem 1479 0 3 0203 100000 99798 99798s0038lrem 1572 0 0 0000 100000 100000 100000s0039lrem 2088 0 0 0000 100000 100000 100000s0042lrem 1815 0 0 0000 100000 100000 100000s0043lrem 1212 0 0 0000 100000 100000 100000s0044lrem 1812 0 0 0000 100000 100000 100000s0045lrem 1968 0 0 0000 100000 100000 100000s0046lrem 1944 0 0 0000 100000 100000 100000s0047lrem 2651 1 0 0038 99962 100000 99962s0049lrem 2040 0 0 0000 100000 100000 100000s0050lrem 1461 3 0 0205 99795 100000 99795s0051lrem 1912 0 2 0105 100000 99896 99896s0052lrem 1356 0 0 0000 100000 100000 100000s0053lrem 2148 0 0 0000 100000 100000 100000s0054lrem 1979 31 2 1668 98458 99899 98360s0055lrem 1381 0 1 0072 100000 99928 99928s0056lrem 1732 0 0 0000 100000 100000 100000s0057lrem 1896 0 0 0000 100000 100000 100000s0058lrem 2017 0 1 0050 100000 99950 99950s0059lrem 1800 0 0 0000 100000 100000 100000s0060lrem 140 0 0 0000 100000 100000 100000s0062lrem 1488 0 0 0000 100000 100000 100000s0063lrem 1845 3 0 0163 99838 100000 99838s0064lrem 1797 3 0 0167 99833 100000 99833s0065lrem 1704 0 0 0000 100000 100000 100000s0066lrem 1513 0 1 0066 100000 99934 99934s0067lrem 424 0 4 0943 100000 99065 99065s0068lrem 1377 5 15 1452 99638 98922 98568s0069lrem 1188 0 0 0000 100000 100000 100000s0070lrem 1983 0 1 0050 100000 99950 99950s0071lrem 1848 0 0 0000 100000 100000 100000s0072lrem 2040 0 0 0000 100000 100000 100000s0073lrem 2125 5 0 0235 99765 100000 99765s0074lrem 1140 0 0 0000 100000 100000 100000s0075lrem 1453 0 1 0069 100000 99931 99931s0076lrem 1308 0 0 0000 100000 100000 100000s0077lrem 1692 0 0 0000 100000 100000 100000s0078lrem 1225 0 1 0082 100000 99918 99918

6 Computational and Mathematical Methods in Medicine

Table 1 Continued

Case TP FN FP DER Se +P Accs0079lrem 1620 0 0 0000 100000 100000 100000s0080lrem 1556 0 0 0000 100000 100000 100000s0082lrem 1602 0 0 0000 100000 100000 100000s0083lrem 1465 1 0 0068 99932 100000 99932s0084lrem 1464 0 0 0000 100000 100000 100000s0085lrem 1276 0 4 0313 100000 99688 99688s0097lrem 2133 0 1 0047 100000 99953 99953s0101lrem 1500 0 0 0000 100000 100000 100000s0103lrem 1273 0 2 0157 100000 99843 99843s0149lrem 1572 0 0 0000 100000 100000 100000s0152lrem 1532 4 0 0261 99740 100000 99740s0087lrem 1654 12 0 0726 99280 100000 99280s0088lrem 1728 0 0 0000 100000 100000 100000s0091lrem 1380 1 1 0145 99928 99928 99855s0095lrem 1797 3 0 0167 99833 100000 99833s0096lrem 2603 1 0 0038 99962 100000 99962s0150lrem 1583 1 0 0063 99937 100000 99937s0090lrem 1358 0 2 0147 100000 99853 99853s0093lrem 1249 0 1 0080 100000 99920 99920s0291lrem 1548 0 0 0000 100000 100000 100000s0292lrem 1584 0 0 0000 100000 100000 100000Total 119054 91 93 0155 99924 99922 99846

0

43000420004100040000390003800037000Sample

Am

plitu

def[n

]

minus1

(a)

31

43000420004100040000390003800037000Sample

Am

plitu

des[n]

minus1

(b)

1

43000420004100040000390003800037000Sample

Am

plitu

dee[n]

minus1

(c)

Am

plitu

de 02

43000420004100040000390003800037000Sample

minus02minus06minus1

(d)

Figure 5 Process of preparation of ECG signal (record s0292lrem lead avr)

In order to define performance and efficiency of the algo-rithm the ClassificationAccuracy (Acc) Positive Predictivity(+P) sensitivity (Se) and detection error rate were calculatedby using the following equations

Acc = TPTP + FN + FP

times 100

+P = TPTP + FP

times 100

Se = TPTP + FN

times 100

DER = FP + FNTP

times 100

(10)

Here TP defines a true detected peak by the algorithmFN (false negative) is the number of not detected R peaksand FP (false positive) is the number of noise spikes detectedas R peaks [3 30]

Figures 4(a) and 5(a) show the output after the band-pass filter 119891[119899] and normalized amplitude 119886[119899] Figures4(b) and 5(b) show Shannon energy 119904[119899] and normalizedamplitude and Figures 4(c) and 5(c) show after envelope119890[119899] signal QRS complex of ECG signal is shown inFigures 4(d) and 5(d) Red line defines a detected peak 119910-axis represents the amplitude and 119909-axis represents thesample

In this study the proposed technique is tested on 840leads of PTB Diagnostic ECGDatabase (PTBDB) and values

Computational and Mathematical Methods in Medicine 7

v5

210

440004200040000380003600034000

avl 02

0

440004200040000380003600034000minus02

avf 05

0

440004200040000380003600034000minus05

avr

440004200040000380003600034000

minus06minus1

minus0202

i

060402

0

440004200040000380003600034000

minus02minus04

v1

0

440004200040000380003600034000

04

minus04minus08

v2

10

440004200040000380003600034000minus2minus1

v4440004200040000380003600034000

minus15

05

minus05

115

0

440004200040000380003600034000

av6

05

minus05

v3

10

440004200040000380003600034000minus2minus1iii

10602

440004200040000380003600034000minus02

ii

105

0

440004200040000380003600034000minus05

Figure 6 Detected QRS complex of ECG data (record s0292lrem) red line defines QRS complex detection 119910-axis represents the amplitudeand 119909-axis represents the sample

Am

plitu

de

0

3400032000300002800026000240002200020000

FPminus05

05

(a)

Am

plitu

de

0

140001200010000800060004000

FN

minus05

05

minus1

(b)

Am

plitu

de

1

0

4800046000440004200040000

FN

minus05

05

(c)

Figure 7 (a) Detected QRS complex of ECG data (record s0020arem lead avf) Records s0020arem and s0020brem include tall and sharpP-wave and T-wave In this case the QRS area has low energy (b) Detected QRS complex of s0087lrem-lead 3 This case includes IrregularRR interval (c) Lead v5 of s0089lrem FN (false negative) is the number of not detected R peaks and FP (false positive) is the number ofnoise spikes detected as R peaks 119910-axis represents the amplitude and 119909-axis represents the sample

8 Computational and Mathematical Methods in MedicineTa

ble2Th

eQRS

detectionof

the12channelsof

theP

TBdatabase

(a)

Leads

s0010rem

s0014lrem

s0015lrem

s0017lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i52

00

166

00

151

00

139

00

ii52

00

165

00

151

00

142

03

iii52

00

165

00

152

01

139

00

avr

520

0166

00

152

00

139

00

avl

520

0166

00

152

01

139

00

avf

520

0165

00

151

00

140

01

v152

00

165

00

151

00

140

01

v252

00

166

00

151

00

139

00

v352

00

166

00

151

00

139

00

v452

00

166

00

151

00

139

00

v552

00

166

00

151

00

139

00

v652

00

165

00

151

00

139

00

Total

624

00

1987

00

1815

02

1673

05

(b)

Leads

s0020arem

s0020brem

s0021arem

s0021brem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

156

00

184

00

183

00

ii159

00

156

00

184

00

183

00

iii159

00

156

00

184

00

183

00

avr

159

00

156

00

184

00

183

00

avl

159

00

156

00

184

00

183

00

avf

158

321

151

520

184

00

183

00

v1159

00

156

00

184

00

183

00

v2159

00

156

00

184

00

183

00

v3159

00

156

00

183

10

183

00

v4158

10

156

00

184

00

183

00

v5159

00

156

00

184

00

183

00

v6159

00

156

00

184

00

183

00

Total

1906

421

1867

520

2207

10

2196

00

(c)

Leads

s0025lrem

s0029lrem

s0031lrem

s0035rem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i199

00

136

00

176

00

460

0ii

199

00

137

00

176

00

460

0iii

197

20

137

00

176

00

460

0avr

197

20

137

00

176

00

460

0avl

197

20

136

00

175

10

460

0avf

199

00

137

00

176

00

460

0v1

199

00

136

00

176

00

460

0v2

199

00

136

00

176

00

460

0v3

199

00

137

00

176

00

460

0v4

199

00

136

00

176

00

460

0v5

199

00

137

00

176

00

460

0v6

199

00

136

00

176

00

460

0Total

2382

60

1638

00

2111

10

552

00

Computational and Mathematical Methods in Medicine 9(d)

Leads

s0036lrem

s0037lrem

s0038lrem

s0039lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i173

01

123

00

131

00

174

00

ii172

00

123

00

131

00

174

00

iii172

00

124

01

131

00

174

00

avr

173

01

123

00

131

00

174

00

avl

172

00

124

01

131

00

174

00

avf

172

00

124

01

131

00

174

00

v1172

00

123

00

131

00

174

00

v2172

00

123

00

131

00

174

00

v3172

00

123

00

131

00

174

00

v4172

00

123

00

131

00

174

00

v5172

00

123

00

131

00

174

00

v6172

00

123

00

131

00

174

00

Total

2066

02

1479

03

1572

00

2088

00

(e)

Leads

s004

2lrem

s0043lrem

s004

4lrem

s0045lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i152

00

101

00

151

00

164

00

ii151

00

101

00

151

00

164

00

iii151

00

101

00

151

00

164

00

avr

152

00

101

00

151

00

164

00

avl

152

00

101

00

151

00

164

00

avf

151

00

101

00

151

00

164

00

v1151

00

101

00

151

00

164

00

v2151

00

101

00

151

00

164

00

v3151

00

101

00

151

00

164

00

v4151

00

101

00

151

00

164

00

v5151

00

101

00

151

00

164

00

v6151

00

101

00

151

00

164

00

Total

1815

00

1212

00

1812

00

1968

00

(f)

Leads

s004

6lrem

s0047lrem

s0049lrem

s0050lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i162

00

221

00

170

00

121

10

ii162

00

221

00

170

00

121

10

iii162

00

221

00

170

00

122

00

avr

162

00

221

00

170

00

122

00

avl

162

00

221

00

170

00

122

00

avf

162

00

221

00

170

00

122

00

v1162

00

221

00

170

00

122

00

v2162

00

221

00

170

00

122

00

v3162

00

221

00

170

00

122

00

v4162

00

220

10

170

00

122

00

v5162

00

221

00

170

00

122

00

v6162

00

221

00

170

00

121

10

Total

1944

00

2651

10

2040

00

1461

30

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

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Page 2: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

2 Computational and Mathematical Methods in Medicine

PRsegment

STsegment

PT

RPinterval

QTinterval

QS

QRScomplex

Figure 1 Schematic diagram of normal ECG

has a leakage in practical tasks [13] Filter bank [14] a HiddenMarkov Model (HMM) describes the process where directobservation is not possible when sequence of symbols canobserve HMM It is used in many fields such as classificationof heartbeat and apnea bradycardia detection in preterminfants [15] Hermite Transform (HT) was recently usedinstead of Fourier Transform HT shows better performancewhen optimization is done properly [16] Threshold method[17] Shannon energy envelope (SEE) is the average spectrumof energy and is better able to detect peaks in case of variousQRS polarities and sudden changes in QRS amplitude SEEdetects R-peak with a better estimate [18] S-Transform andShannon energy (SSE) create a frequency-dependent regula-tion which is directly related with the Fourier spectrum S-Transform includes short time Fourier Transform (STFT) andtheWavelet Transform (WT) SSE gives a smooth cover for P-waves and T-waves and completely decreases their influence[19] Methods such as pattern matching are based on theircomparing and contrastingThe calculations are complex andneed manual classification [6]

In this paper an algorithm based on Shannon energyhas been proposed to improve the QRS complex detectionand simplify the detection process First a band-pass filter isused for eliminating noise Second Shannon energy of ECGsignal is calculated Third include moving averages and adifferential for the envelope of step 2 Finally with defininga threshold peaks are detected The proposed algorithm istested on 115-second (to end) ECG signal of PTB DiagnosticECG Database (PTBDB) [20 21] and detection accuracy of99846 is obtainedThe proposed technique results in goodperformance without being mathematically complex

2 Method

The block diagram of detecting QRS complex algorithm isshown in Figure 2 It includes four stages Stage 1 includesband-pass digital filter and amplitude normalization Stage

2 includes calculating Shannon energy of stage 1 In stage3 with moving average and differencing make a pack ofShannon energy and in stage 4 with defining a thresholdQRS complex is detected

21 Preparations Signal Digital-analog conversion processis causing all kinds of noise interference and sometimesstrongly affects the information These interactions includefrequency interference muscle contraction and wanderingsignals from the baseline or Gaussian white noise [5]

The ECG signal recorded from human beings is a poorsignal and is often contaminated by noise Frequency interfer-ence includes a narrow band from 48 to 60Hz and harmonicinterference and the noise frommuscle contraction occurs in38 to 45Hz To eliminate this noise notch filter is good [22]Deep breathing loosely connected electrodes and suddenchanges in voltage lead the baseline signal to be wondered(baseline drift) [5] Random variable vector (mean) and chro-matogram baseline estimation and denoising using sparsity(BEADS) algorithm [23] are good methods to eliminatebaseline drift The band-pass filter decreases efficacy ofmuscle contraction frequency interference baseline driftand P-wave and T-wave interference [7 24] To repress thesenoises Butterworth band-pass digital filter with stop-pointset at 5 to 16Hz is used Butterworth has no ripple in band-pass [25] After band-pass filter the signal is normalizedwith(1) in stage 1 [26]

119886 [119899] =1003816100381610038161003816119891 [119899]1003816100381610038161003816

max119873119894=1

1003816100381610038161003816119891 [119899]1003816100381610038161003816 (1)

where 119886[119899] is a normalized amplitude 119891[119899] is an afterprocesses band-pass filter (BPF) 119873 denotes the number ofsamples

22 Shannon Energy and Detection of QRS Complex Theproposed method is based on the use of signal energy The

Computational and Mathematical Methods in Medicine 3

Band-pass filter(BPF)

Amplitudenormalization

(AN)

Stage 2Shannon energy

(SE)

Moving average(MA)

Amplitudenormalization

(AN)Envelope

Stage 4 QRS complex detection in ECGsignal

Stage 1

Stage 3

s[n] s[n]

s[n]

f[n]ECG x[n] f[n]

Figure 2 Block diagram to detect QRS complex

signal square is very close to the signal energy For discretetime signal energy is defined as follows

119864119909=infin

summinusinfin

119909 (119899) 119909lowast (119899) =infin

summinusinfin

|119909 (119899)|2 (2)

Here 119864119909expresses the signal energy 119909(119899) defined ECG

data and 119899 is samplessum represents sum from (minusinfin infin) [27]To explain we have the following

119864119909= (11990920+ 11990921+ 11990922+ 11990923+ sdot sdot sdot) (3)

Shannon energy calculates the average spectrum of thesignal energy In other words discount the high componentsinto the low components So input amplitude is not impor-tant Shannon energy andHilbert Transform (SEHT) providea good accessory for detecting R-peak but this techniquehas a problem SEHT needs high memory and has delays[28] It is designed for solving our actual requirements Tofind smooth Shannon energy zero-phase filter and Shannonenergy approximate are playing a basic role [24 28]

Shannon energy (SE) calculates the energy of the localspectrum for each sample Below is a calculation of Shannonenergy

SE = minus |119886 [119899]| log (|119886 [119899]|)

119904 [119899] = minus1198862 [119899] log (1198862 [119899]) (4)

where 119886[119899] is after process normalizationEnergy that better approaches detection ranges in pres-

ence of noise or domains with more width results in fewererrors Capacity to emphasize medium is the advantage ofusing Shannon energy rather than classic energy [18 19] Theselected signal is normalized with (5) in stage 3 for decreasingthe signal base and placing the signal below the baseline

119904 [119899] = 119904 [119899] minus 120583120590 (5)

where 120583 is the random variable vector and 120590 defined standarddeviation of the signal

In stage 3 after computing Shannon energy small spikesaround the main peak of the energy are generated Thesespikes make main peaks detection difficult To eliminatethis spike Shannon energy is converted into energy package(Shannon energy envelope (SEE)) To overcome this problemthe Hilbert Transform is used SEHT method is a simple andhigh accessory but the SEHT needs high memory and hasdelays so it is unfit for real time detection [24 28] To smoothout the spikes rectangular (ℎ) with 119871 length is used Filteringoperation is shown as follows

119898[119899] = filter (ℎ 119895 119878)

1198981015840 [119899] = filter (ℎ 119895 119878) (6)

where 119898[119899] defines moving average 119895 is a constant and119878 defines Shannon energy from previous steps For spikesreducing and enveloping the nonzero peaks obtained fromdifferential get linked In other words diagnosed peaks arelinked together

Difference is defined below

119889 [119899] = 119891 [119899] minus 119891 [119899 minus 1] 119899 = 2 3 (7)

The sign is defined as follows

sgn (119909)

minus1 if 119909 lt 00 if 119909 = 01 if 119909 gt 0

(8)

where 119909 is a real numberIn stage 4 positive peaks are QRS complex location To

detect QRS complex a threshold (see (9)) is defined In

4 Computational and Mathematical Methods in Medicine

(a) (b)

Figure 3 Simulation result Time and number of peaks detection in each lead are shown ((a) record s0292lrem (b) record s0291lrem)

Sample

005

48000460004400042000minus1

minus05

Am

plitu

def[n

]

(a)

Sample

31

48000460004400042000minus1

Am

plitu

des[n]

(b)

Sample

20

48000460004400042000minus2

Am

plitu

dee[n]

(c)

Am

plitu

de

Sample

0

48000460004400042000minus2

(d)

Figure 4 Process of preparations of ECG signal (record s0291lrem lead v3)

fact samples with greater amplitude than the threshold areselected as output

threshold = 10038161003816100381610038161003816120581120583 (1 minus 1205902)10038161003816100381610038161003816 if 120590 lt 120583

threshold = 10038161003816100381610038161003816120581120590 (1 minus 1205832)10038161003816100381610038161003816 if 120590 gt 120583(9)

where 120581 is a constant

3 Result

The experimental results are obtained after simulation on 70healthy patientsrsquo signals for all 12 leads and using PTB Diag-nostic ECGDatabase (PTBDB)The Physikalisch-TechnischeBundesanstalt (PTB) is the National Metrology Institute ofGermany PTB database is provided for PhysioNet and hasdifferent morphologies The ECGs in this database obtain 15input channels including the conventional 12 leads (i ii iiiavr avl avf v1 v2 v3 v4 v5 and v6) together with the 3

Frank lead ECGs (vx vy and vz) Input voltage is plusmn16mVinput resistance is 100Ω ADC resolution is equal to 16 bitswith 05120583LSB and sampling frequency is equal to 1 KHz[20 21]The proposed algorithmwas performed on a 24GHzIntel core i3 CPU using GNU Octave version 402 [29] Aselected signal from patient 117 has a variety of physiologyand baseline drift Leads (i ii avl avf v3 v4 v5 and v6) ofrecord s0291lrem and leads (i ii iii avf v1 v2 v4 v5 and v6)of record s0292lrem have high amplitude Leads (i avl v2 v3and v4) of record s0291lrem and leads (avr avl and avf) ofrecord s0292lrem have a sharp and tall T-wave

Figure 3 shows the result of simulation to detect each leadof patient 117 in Octave Figures 4 and 5 show the process ofECG signal provision and peak detectionThe QRS detectionof the 12 channels of healthy ECG signal in patient 117 ofthe PTB database is reported in Table 1 and the AppendixDetection of the 12 leads is shown in Figure 6 Figure 7 shows3 leads of 3 cases

Computational and Mathematical Methods in Medicine 5

Table 1 The QRS detection of ECG signal of the PTB database

Case TP FN FP DER Se +P Accs0010 rem 624 0 0 0000 100000 100000 100000s0014lrem 1987 0 0 0000 100000 100000 100000s0015lrem 1815 0 2 0110 100000 99890 99890s0017lrem 1673 0 5 0299 100000 99702 99702s0020arem 1906 4 21 1312 99791 98910 98705s0020brem 1867 5 20 1339 99733 98940 98679s0021arem 2207 1 0 0045 99955 100000 99955s0021brem 2196 0 0 0000 100000 100000 100000s0025lrem 2382 6 0 0252 99749 100000 99749s0029lrem 1638 0 0 0000 100000 100000 100000s0031lrem 2111 1 0 0047 99953 100000 99953s0035 rem 552 0 0 0000 100000 100000 100000s0036lrem 2066 0 2 0097 100000 99903 99903s0037lrem 1479 0 3 0203 100000 99798 99798s0038lrem 1572 0 0 0000 100000 100000 100000s0039lrem 2088 0 0 0000 100000 100000 100000s0042lrem 1815 0 0 0000 100000 100000 100000s0043lrem 1212 0 0 0000 100000 100000 100000s0044lrem 1812 0 0 0000 100000 100000 100000s0045lrem 1968 0 0 0000 100000 100000 100000s0046lrem 1944 0 0 0000 100000 100000 100000s0047lrem 2651 1 0 0038 99962 100000 99962s0049lrem 2040 0 0 0000 100000 100000 100000s0050lrem 1461 3 0 0205 99795 100000 99795s0051lrem 1912 0 2 0105 100000 99896 99896s0052lrem 1356 0 0 0000 100000 100000 100000s0053lrem 2148 0 0 0000 100000 100000 100000s0054lrem 1979 31 2 1668 98458 99899 98360s0055lrem 1381 0 1 0072 100000 99928 99928s0056lrem 1732 0 0 0000 100000 100000 100000s0057lrem 1896 0 0 0000 100000 100000 100000s0058lrem 2017 0 1 0050 100000 99950 99950s0059lrem 1800 0 0 0000 100000 100000 100000s0060lrem 140 0 0 0000 100000 100000 100000s0062lrem 1488 0 0 0000 100000 100000 100000s0063lrem 1845 3 0 0163 99838 100000 99838s0064lrem 1797 3 0 0167 99833 100000 99833s0065lrem 1704 0 0 0000 100000 100000 100000s0066lrem 1513 0 1 0066 100000 99934 99934s0067lrem 424 0 4 0943 100000 99065 99065s0068lrem 1377 5 15 1452 99638 98922 98568s0069lrem 1188 0 0 0000 100000 100000 100000s0070lrem 1983 0 1 0050 100000 99950 99950s0071lrem 1848 0 0 0000 100000 100000 100000s0072lrem 2040 0 0 0000 100000 100000 100000s0073lrem 2125 5 0 0235 99765 100000 99765s0074lrem 1140 0 0 0000 100000 100000 100000s0075lrem 1453 0 1 0069 100000 99931 99931s0076lrem 1308 0 0 0000 100000 100000 100000s0077lrem 1692 0 0 0000 100000 100000 100000s0078lrem 1225 0 1 0082 100000 99918 99918

6 Computational and Mathematical Methods in Medicine

Table 1 Continued

Case TP FN FP DER Se +P Accs0079lrem 1620 0 0 0000 100000 100000 100000s0080lrem 1556 0 0 0000 100000 100000 100000s0082lrem 1602 0 0 0000 100000 100000 100000s0083lrem 1465 1 0 0068 99932 100000 99932s0084lrem 1464 0 0 0000 100000 100000 100000s0085lrem 1276 0 4 0313 100000 99688 99688s0097lrem 2133 0 1 0047 100000 99953 99953s0101lrem 1500 0 0 0000 100000 100000 100000s0103lrem 1273 0 2 0157 100000 99843 99843s0149lrem 1572 0 0 0000 100000 100000 100000s0152lrem 1532 4 0 0261 99740 100000 99740s0087lrem 1654 12 0 0726 99280 100000 99280s0088lrem 1728 0 0 0000 100000 100000 100000s0091lrem 1380 1 1 0145 99928 99928 99855s0095lrem 1797 3 0 0167 99833 100000 99833s0096lrem 2603 1 0 0038 99962 100000 99962s0150lrem 1583 1 0 0063 99937 100000 99937s0090lrem 1358 0 2 0147 100000 99853 99853s0093lrem 1249 0 1 0080 100000 99920 99920s0291lrem 1548 0 0 0000 100000 100000 100000s0292lrem 1584 0 0 0000 100000 100000 100000Total 119054 91 93 0155 99924 99922 99846

0

43000420004100040000390003800037000Sample

Am

plitu

def[n

]

minus1

(a)

31

43000420004100040000390003800037000Sample

Am

plitu

des[n]

minus1

(b)

1

43000420004100040000390003800037000Sample

Am

plitu

dee[n]

minus1

(c)

Am

plitu

de 02

43000420004100040000390003800037000Sample

minus02minus06minus1

(d)

Figure 5 Process of preparation of ECG signal (record s0292lrem lead avr)

In order to define performance and efficiency of the algo-rithm the ClassificationAccuracy (Acc) Positive Predictivity(+P) sensitivity (Se) and detection error rate were calculatedby using the following equations

Acc = TPTP + FN + FP

times 100

+P = TPTP + FP

times 100

Se = TPTP + FN

times 100

DER = FP + FNTP

times 100

(10)

Here TP defines a true detected peak by the algorithmFN (false negative) is the number of not detected R peaksand FP (false positive) is the number of noise spikes detectedas R peaks [3 30]

Figures 4(a) and 5(a) show the output after the band-pass filter 119891[119899] and normalized amplitude 119886[119899] Figures4(b) and 5(b) show Shannon energy 119904[119899] and normalizedamplitude and Figures 4(c) and 5(c) show after envelope119890[119899] signal QRS complex of ECG signal is shown inFigures 4(d) and 5(d) Red line defines a detected peak 119910-axis represents the amplitude and 119909-axis represents thesample

In this study the proposed technique is tested on 840leads of PTB Diagnostic ECGDatabase (PTBDB) and values

Computational and Mathematical Methods in Medicine 7

v5

210

440004200040000380003600034000

avl 02

0

440004200040000380003600034000minus02

avf 05

0

440004200040000380003600034000minus05

avr

440004200040000380003600034000

minus06minus1

minus0202

i

060402

0

440004200040000380003600034000

minus02minus04

v1

0

440004200040000380003600034000

04

minus04minus08

v2

10

440004200040000380003600034000minus2minus1

v4440004200040000380003600034000

minus15

05

minus05

115

0

440004200040000380003600034000

av6

05

minus05

v3

10

440004200040000380003600034000minus2minus1iii

10602

440004200040000380003600034000minus02

ii

105

0

440004200040000380003600034000minus05

Figure 6 Detected QRS complex of ECG data (record s0292lrem) red line defines QRS complex detection 119910-axis represents the amplitudeand 119909-axis represents the sample

Am

plitu

de

0

3400032000300002800026000240002200020000

FPminus05

05

(a)

Am

plitu

de

0

140001200010000800060004000

FN

minus05

05

minus1

(b)

Am

plitu

de

1

0

4800046000440004200040000

FN

minus05

05

(c)

Figure 7 (a) Detected QRS complex of ECG data (record s0020arem lead avf) Records s0020arem and s0020brem include tall and sharpP-wave and T-wave In this case the QRS area has low energy (b) Detected QRS complex of s0087lrem-lead 3 This case includes IrregularRR interval (c) Lead v5 of s0089lrem FN (false negative) is the number of not detected R peaks and FP (false positive) is the number ofnoise spikes detected as R peaks 119910-axis represents the amplitude and 119909-axis represents the sample

8 Computational and Mathematical Methods in MedicineTa

ble2Th

eQRS

detectionof

the12channelsof

theP

TBdatabase

(a)

Leads

s0010rem

s0014lrem

s0015lrem

s0017lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i52

00

166

00

151

00

139

00

ii52

00

165

00

151

00

142

03

iii52

00

165

00

152

01

139

00

avr

520

0166

00

152

00

139

00

avl

520

0166

00

152

01

139

00

avf

520

0165

00

151

00

140

01

v152

00

165

00

151

00

140

01

v252

00

166

00

151

00

139

00

v352

00

166

00

151

00

139

00

v452

00

166

00

151

00

139

00

v552

00

166

00

151

00

139

00

v652

00

165

00

151

00

139

00

Total

624

00

1987

00

1815

02

1673

05

(b)

Leads

s0020arem

s0020brem

s0021arem

s0021brem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

156

00

184

00

183

00

ii159

00

156

00

184

00

183

00

iii159

00

156

00

184

00

183

00

avr

159

00

156

00

184

00

183

00

avl

159

00

156

00

184

00

183

00

avf

158

321

151

520

184

00

183

00

v1159

00

156

00

184

00

183

00

v2159

00

156

00

184

00

183

00

v3159

00

156

00

183

10

183

00

v4158

10

156

00

184

00

183

00

v5159

00

156

00

184

00

183

00

v6159

00

156

00

184

00

183

00

Total

1906

421

1867

520

2207

10

2196

00

(c)

Leads

s0025lrem

s0029lrem

s0031lrem

s0035rem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i199

00

136

00

176

00

460

0ii

199

00

137

00

176

00

460

0iii

197

20

137

00

176

00

460

0avr

197

20

137

00

176

00

460

0avl

197

20

136

00

175

10

460

0avf

199

00

137

00

176

00

460

0v1

199

00

136

00

176

00

460

0v2

199

00

136

00

176

00

460

0v3

199

00

137

00

176

00

460

0v4

199

00

136

00

176

00

460

0v5

199

00

137

00

176

00

460

0v6

199

00

136

00

176

00

460

0Total

2382

60

1638

00

2111

10

552

00

Computational and Mathematical Methods in Medicine 9(d)

Leads

s0036lrem

s0037lrem

s0038lrem

s0039lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i173

01

123

00

131

00

174

00

ii172

00

123

00

131

00

174

00

iii172

00

124

01

131

00

174

00

avr

173

01

123

00

131

00

174

00

avl

172

00

124

01

131

00

174

00

avf

172

00

124

01

131

00

174

00

v1172

00

123

00

131

00

174

00

v2172

00

123

00

131

00

174

00

v3172

00

123

00

131

00

174

00

v4172

00

123

00

131

00

174

00

v5172

00

123

00

131

00

174

00

v6172

00

123

00

131

00

174

00

Total

2066

02

1479

03

1572

00

2088

00

(e)

Leads

s004

2lrem

s0043lrem

s004

4lrem

s0045lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i152

00

101

00

151

00

164

00

ii151

00

101

00

151

00

164

00

iii151

00

101

00

151

00

164

00

avr

152

00

101

00

151

00

164

00

avl

152

00

101

00

151

00

164

00

avf

151

00

101

00

151

00

164

00

v1151

00

101

00

151

00

164

00

v2151

00

101

00

151

00

164

00

v3151

00

101

00

151

00

164

00

v4151

00

101

00

151

00

164

00

v5151

00

101

00

151

00

164

00

v6151

00

101

00

151

00

164

00

Total

1815

00

1212

00

1812

00

1968

00

(f)

Leads

s004

6lrem

s0047lrem

s0049lrem

s0050lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i162

00

221

00

170

00

121

10

ii162

00

221

00

170

00

121

10

iii162

00

221

00

170

00

122

00

avr

162

00

221

00

170

00

122

00

avl

162

00

221

00

170

00

122

00

avf

162

00

221

00

170

00

122

00

v1162

00

221

00

170

00

122

00

v2162

00

221

00

170

00

122

00

v3162

00

221

00

170

00

122

00

v4162

00

220

10

170

00

122

00

v5162

00

221

00

170

00

122

00

v6162

00

221

00

170

00

121

10

Total

1944

00

2651

10

2040

00

1461

30

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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of

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Behavioural Neurology

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Disease Markers

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Oxidative Medicine and Cellular Longevity

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Computational and Mathematical Methods in Medicine

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Research and TreatmentAIDS

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Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 3: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

Computational and Mathematical Methods in Medicine 3

Band-pass filter(BPF)

Amplitudenormalization

(AN)

Stage 2Shannon energy

(SE)

Moving average(MA)

Amplitudenormalization

(AN)Envelope

Stage 4 QRS complex detection in ECGsignal

Stage 1

Stage 3

s[n] s[n]

s[n]

f[n]ECG x[n] f[n]

Figure 2 Block diagram to detect QRS complex

signal square is very close to the signal energy For discretetime signal energy is defined as follows

119864119909=infin

summinusinfin

119909 (119899) 119909lowast (119899) =infin

summinusinfin

|119909 (119899)|2 (2)

Here 119864119909expresses the signal energy 119909(119899) defined ECG

data and 119899 is samplessum represents sum from (minusinfin infin) [27]To explain we have the following

119864119909= (11990920+ 11990921+ 11990922+ 11990923+ sdot sdot sdot) (3)

Shannon energy calculates the average spectrum of thesignal energy In other words discount the high componentsinto the low components So input amplitude is not impor-tant Shannon energy andHilbert Transform (SEHT) providea good accessory for detecting R-peak but this techniquehas a problem SEHT needs high memory and has delays[28] It is designed for solving our actual requirements Tofind smooth Shannon energy zero-phase filter and Shannonenergy approximate are playing a basic role [24 28]

Shannon energy (SE) calculates the energy of the localspectrum for each sample Below is a calculation of Shannonenergy

SE = minus |119886 [119899]| log (|119886 [119899]|)

119904 [119899] = minus1198862 [119899] log (1198862 [119899]) (4)

where 119886[119899] is after process normalizationEnergy that better approaches detection ranges in pres-

ence of noise or domains with more width results in fewererrors Capacity to emphasize medium is the advantage ofusing Shannon energy rather than classic energy [18 19] Theselected signal is normalized with (5) in stage 3 for decreasingthe signal base and placing the signal below the baseline

119904 [119899] = 119904 [119899] minus 120583120590 (5)

where 120583 is the random variable vector and 120590 defined standarddeviation of the signal

In stage 3 after computing Shannon energy small spikesaround the main peak of the energy are generated Thesespikes make main peaks detection difficult To eliminatethis spike Shannon energy is converted into energy package(Shannon energy envelope (SEE)) To overcome this problemthe Hilbert Transform is used SEHT method is a simple andhigh accessory but the SEHT needs high memory and hasdelays so it is unfit for real time detection [24 28] To smoothout the spikes rectangular (ℎ) with 119871 length is used Filteringoperation is shown as follows

119898[119899] = filter (ℎ 119895 119878)

1198981015840 [119899] = filter (ℎ 119895 119878) (6)

where 119898[119899] defines moving average 119895 is a constant and119878 defines Shannon energy from previous steps For spikesreducing and enveloping the nonzero peaks obtained fromdifferential get linked In other words diagnosed peaks arelinked together

Difference is defined below

119889 [119899] = 119891 [119899] minus 119891 [119899 minus 1] 119899 = 2 3 (7)

The sign is defined as follows

sgn (119909)

minus1 if 119909 lt 00 if 119909 = 01 if 119909 gt 0

(8)

where 119909 is a real numberIn stage 4 positive peaks are QRS complex location To

detect QRS complex a threshold (see (9)) is defined In

4 Computational and Mathematical Methods in Medicine

(a) (b)

Figure 3 Simulation result Time and number of peaks detection in each lead are shown ((a) record s0292lrem (b) record s0291lrem)

Sample

005

48000460004400042000minus1

minus05

Am

plitu

def[n

]

(a)

Sample

31

48000460004400042000minus1

Am

plitu

des[n]

(b)

Sample

20

48000460004400042000minus2

Am

plitu

dee[n]

(c)

Am

plitu

de

Sample

0

48000460004400042000minus2

(d)

Figure 4 Process of preparations of ECG signal (record s0291lrem lead v3)

fact samples with greater amplitude than the threshold areselected as output

threshold = 10038161003816100381610038161003816120581120583 (1 minus 1205902)10038161003816100381610038161003816 if 120590 lt 120583

threshold = 10038161003816100381610038161003816120581120590 (1 minus 1205832)10038161003816100381610038161003816 if 120590 gt 120583(9)

where 120581 is a constant

3 Result

The experimental results are obtained after simulation on 70healthy patientsrsquo signals for all 12 leads and using PTB Diag-nostic ECGDatabase (PTBDB)The Physikalisch-TechnischeBundesanstalt (PTB) is the National Metrology Institute ofGermany PTB database is provided for PhysioNet and hasdifferent morphologies The ECGs in this database obtain 15input channels including the conventional 12 leads (i ii iiiavr avl avf v1 v2 v3 v4 v5 and v6) together with the 3

Frank lead ECGs (vx vy and vz) Input voltage is plusmn16mVinput resistance is 100Ω ADC resolution is equal to 16 bitswith 05120583LSB and sampling frequency is equal to 1 KHz[20 21]The proposed algorithmwas performed on a 24GHzIntel core i3 CPU using GNU Octave version 402 [29] Aselected signal from patient 117 has a variety of physiologyand baseline drift Leads (i ii avl avf v3 v4 v5 and v6) ofrecord s0291lrem and leads (i ii iii avf v1 v2 v4 v5 and v6)of record s0292lrem have high amplitude Leads (i avl v2 v3and v4) of record s0291lrem and leads (avr avl and avf) ofrecord s0292lrem have a sharp and tall T-wave

Figure 3 shows the result of simulation to detect each leadof patient 117 in Octave Figures 4 and 5 show the process ofECG signal provision and peak detectionThe QRS detectionof the 12 channels of healthy ECG signal in patient 117 ofthe PTB database is reported in Table 1 and the AppendixDetection of the 12 leads is shown in Figure 6 Figure 7 shows3 leads of 3 cases

Computational and Mathematical Methods in Medicine 5

Table 1 The QRS detection of ECG signal of the PTB database

Case TP FN FP DER Se +P Accs0010 rem 624 0 0 0000 100000 100000 100000s0014lrem 1987 0 0 0000 100000 100000 100000s0015lrem 1815 0 2 0110 100000 99890 99890s0017lrem 1673 0 5 0299 100000 99702 99702s0020arem 1906 4 21 1312 99791 98910 98705s0020brem 1867 5 20 1339 99733 98940 98679s0021arem 2207 1 0 0045 99955 100000 99955s0021brem 2196 0 0 0000 100000 100000 100000s0025lrem 2382 6 0 0252 99749 100000 99749s0029lrem 1638 0 0 0000 100000 100000 100000s0031lrem 2111 1 0 0047 99953 100000 99953s0035 rem 552 0 0 0000 100000 100000 100000s0036lrem 2066 0 2 0097 100000 99903 99903s0037lrem 1479 0 3 0203 100000 99798 99798s0038lrem 1572 0 0 0000 100000 100000 100000s0039lrem 2088 0 0 0000 100000 100000 100000s0042lrem 1815 0 0 0000 100000 100000 100000s0043lrem 1212 0 0 0000 100000 100000 100000s0044lrem 1812 0 0 0000 100000 100000 100000s0045lrem 1968 0 0 0000 100000 100000 100000s0046lrem 1944 0 0 0000 100000 100000 100000s0047lrem 2651 1 0 0038 99962 100000 99962s0049lrem 2040 0 0 0000 100000 100000 100000s0050lrem 1461 3 0 0205 99795 100000 99795s0051lrem 1912 0 2 0105 100000 99896 99896s0052lrem 1356 0 0 0000 100000 100000 100000s0053lrem 2148 0 0 0000 100000 100000 100000s0054lrem 1979 31 2 1668 98458 99899 98360s0055lrem 1381 0 1 0072 100000 99928 99928s0056lrem 1732 0 0 0000 100000 100000 100000s0057lrem 1896 0 0 0000 100000 100000 100000s0058lrem 2017 0 1 0050 100000 99950 99950s0059lrem 1800 0 0 0000 100000 100000 100000s0060lrem 140 0 0 0000 100000 100000 100000s0062lrem 1488 0 0 0000 100000 100000 100000s0063lrem 1845 3 0 0163 99838 100000 99838s0064lrem 1797 3 0 0167 99833 100000 99833s0065lrem 1704 0 0 0000 100000 100000 100000s0066lrem 1513 0 1 0066 100000 99934 99934s0067lrem 424 0 4 0943 100000 99065 99065s0068lrem 1377 5 15 1452 99638 98922 98568s0069lrem 1188 0 0 0000 100000 100000 100000s0070lrem 1983 0 1 0050 100000 99950 99950s0071lrem 1848 0 0 0000 100000 100000 100000s0072lrem 2040 0 0 0000 100000 100000 100000s0073lrem 2125 5 0 0235 99765 100000 99765s0074lrem 1140 0 0 0000 100000 100000 100000s0075lrem 1453 0 1 0069 100000 99931 99931s0076lrem 1308 0 0 0000 100000 100000 100000s0077lrem 1692 0 0 0000 100000 100000 100000s0078lrem 1225 0 1 0082 100000 99918 99918

6 Computational and Mathematical Methods in Medicine

Table 1 Continued

Case TP FN FP DER Se +P Accs0079lrem 1620 0 0 0000 100000 100000 100000s0080lrem 1556 0 0 0000 100000 100000 100000s0082lrem 1602 0 0 0000 100000 100000 100000s0083lrem 1465 1 0 0068 99932 100000 99932s0084lrem 1464 0 0 0000 100000 100000 100000s0085lrem 1276 0 4 0313 100000 99688 99688s0097lrem 2133 0 1 0047 100000 99953 99953s0101lrem 1500 0 0 0000 100000 100000 100000s0103lrem 1273 0 2 0157 100000 99843 99843s0149lrem 1572 0 0 0000 100000 100000 100000s0152lrem 1532 4 0 0261 99740 100000 99740s0087lrem 1654 12 0 0726 99280 100000 99280s0088lrem 1728 0 0 0000 100000 100000 100000s0091lrem 1380 1 1 0145 99928 99928 99855s0095lrem 1797 3 0 0167 99833 100000 99833s0096lrem 2603 1 0 0038 99962 100000 99962s0150lrem 1583 1 0 0063 99937 100000 99937s0090lrem 1358 0 2 0147 100000 99853 99853s0093lrem 1249 0 1 0080 100000 99920 99920s0291lrem 1548 0 0 0000 100000 100000 100000s0292lrem 1584 0 0 0000 100000 100000 100000Total 119054 91 93 0155 99924 99922 99846

0

43000420004100040000390003800037000Sample

Am

plitu

def[n

]

minus1

(a)

31

43000420004100040000390003800037000Sample

Am

plitu

des[n]

minus1

(b)

1

43000420004100040000390003800037000Sample

Am

plitu

dee[n]

minus1

(c)

Am

plitu

de 02

43000420004100040000390003800037000Sample

minus02minus06minus1

(d)

Figure 5 Process of preparation of ECG signal (record s0292lrem lead avr)

In order to define performance and efficiency of the algo-rithm the ClassificationAccuracy (Acc) Positive Predictivity(+P) sensitivity (Se) and detection error rate were calculatedby using the following equations

Acc = TPTP + FN + FP

times 100

+P = TPTP + FP

times 100

Se = TPTP + FN

times 100

DER = FP + FNTP

times 100

(10)

Here TP defines a true detected peak by the algorithmFN (false negative) is the number of not detected R peaksand FP (false positive) is the number of noise spikes detectedas R peaks [3 30]

Figures 4(a) and 5(a) show the output after the band-pass filter 119891[119899] and normalized amplitude 119886[119899] Figures4(b) and 5(b) show Shannon energy 119904[119899] and normalizedamplitude and Figures 4(c) and 5(c) show after envelope119890[119899] signal QRS complex of ECG signal is shown inFigures 4(d) and 5(d) Red line defines a detected peak 119910-axis represents the amplitude and 119909-axis represents thesample

In this study the proposed technique is tested on 840leads of PTB Diagnostic ECGDatabase (PTBDB) and values

Computational and Mathematical Methods in Medicine 7

v5

210

440004200040000380003600034000

avl 02

0

440004200040000380003600034000minus02

avf 05

0

440004200040000380003600034000minus05

avr

440004200040000380003600034000

minus06minus1

minus0202

i

060402

0

440004200040000380003600034000

minus02minus04

v1

0

440004200040000380003600034000

04

minus04minus08

v2

10

440004200040000380003600034000minus2minus1

v4440004200040000380003600034000

minus15

05

minus05

115

0

440004200040000380003600034000

av6

05

minus05

v3

10

440004200040000380003600034000minus2minus1iii

10602

440004200040000380003600034000minus02

ii

105

0

440004200040000380003600034000minus05

Figure 6 Detected QRS complex of ECG data (record s0292lrem) red line defines QRS complex detection 119910-axis represents the amplitudeand 119909-axis represents the sample

Am

plitu

de

0

3400032000300002800026000240002200020000

FPminus05

05

(a)

Am

plitu

de

0

140001200010000800060004000

FN

minus05

05

minus1

(b)

Am

plitu

de

1

0

4800046000440004200040000

FN

minus05

05

(c)

Figure 7 (a) Detected QRS complex of ECG data (record s0020arem lead avf) Records s0020arem and s0020brem include tall and sharpP-wave and T-wave In this case the QRS area has low energy (b) Detected QRS complex of s0087lrem-lead 3 This case includes IrregularRR interval (c) Lead v5 of s0089lrem FN (false negative) is the number of not detected R peaks and FP (false positive) is the number ofnoise spikes detected as R peaks 119910-axis represents the amplitude and 119909-axis represents the sample

8 Computational and Mathematical Methods in MedicineTa

ble2Th

eQRS

detectionof

the12channelsof

theP

TBdatabase

(a)

Leads

s0010rem

s0014lrem

s0015lrem

s0017lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i52

00

166

00

151

00

139

00

ii52

00

165

00

151

00

142

03

iii52

00

165

00

152

01

139

00

avr

520

0166

00

152

00

139

00

avl

520

0166

00

152

01

139

00

avf

520

0165

00

151

00

140

01

v152

00

165

00

151

00

140

01

v252

00

166

00

151

00

139

00

v352

00

166

00

151

00

139

00

v452

00

166

00

151

00

139

00

v552

00

166

00

151

00

139

00

v652

00

165

00

151

00

139

00

Total

624

00

1987

00

1815

02

1673

05

(b)

Leads

s0020arem

s0020brem

s0021arem

s0021brem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

156

00

184

00

183

00

ii159

00

156

00

184

00

183

00

iii159

00

156

00

184

00

183

00

avr

159

00

156

00

184

00

183

00

avl

159

00

156

00

184

00

183

00

avf

158

321

151

520

184

00

183

00

v1159

00

156

00

184

00

183

00

v2159

00

156

00

184

00

183

00

v3159

00

156

00

183

10

183

00

v4158

10

156

00

184

00

183

00

v5159

00

156

00

184

00

183

00

v6159

00

156

00

184

00

183

00

Total

1906

421

1867

520

2207

10

2196

00

(c)

Leads

s0025lrem

s0029lrem

s0031lrem

s0035rem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i199

00

136

00

176

00

460

0ii

199

00

137

00

176

00

460

0iii

197

20

137

00

176

00

460

0avr

197

20

137

00

176

00

460

0avl

197

20

136

00

175

10

460

0avf

199

00

137

00

176

00

460

0v1

199

00

136

00

176

00

460

0v2

199

00

136

00

176

00

460

0v3

199

00

137

00

176

00

460

0v4

199

00

136

00

176

00

460

0v5

199

00

137

00

176

00

460

0v6

199

00

136

00

176

00

460

0Total

2382

60

1638

00

2111

10

552

00

Computational and Mathematical Methods in Medicine 9(d)

Leads

s0036lrem

s0037lrem

s0038lrem

s0039lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i173

01

123

00

131

00

174

00

ii172

00

123

00

131

00

174

00

iii172

00

124

01

131

00

174

00

avr

173

01

123

00

131

00

174

00

avl

172

00

124

01

131

00

174

00

avf

172

00

124

01

131

00

174

00

v1172

00

123

00

131

00

174

00

v2172

00

123

00

131

00

174

00

v3172

00

123

00

131

00

174

00

v4172

00

123

00

131

00

174

00

v5172

00

123

00

131

00

174

00

v6172

00

123

00

131

00

174

00

Total

2066

02

1479

03

1572

00

2088

00

(e)

Leads

s004

2lrem

s0043lrem

s004

4lrem

s0045lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i152

00

101

00

151

00

164

00

ii151

00

101

00

151

00

164

00

iii151

00

101

00

151

00

164

00

avr

152

00

101

00

151

00

164

00

avl

152

00

101

00

151

00

164

00

avf

151

00

101

00

151

00

164

00

v1151

00

101

00

151

00

164

00

v2151

00

101

00

151

00

164

00

v3151

00

101

00

151

00

164

00

v4151

00

101

00

151

00

164

00

v5151

00

101

00

151

00

164

00

v6151

00

101

00

151

00

164

00

Total

1815

00

1212

00

1812

00

1968

00

(f)

Leads

s004

6lrem

s0047lrem

s0049lrem

s0050lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i162

00

221

00

170

00

121

10

ii162

00

221

00

170

00

121

10

iii162

00

221

00

170

00

122

00

avr

162

00

221

00

170

00

122

00

avl

162

00

221

00

170

00

122

00

avf

162

00

221

00

170

00

122

00

v1162

00

221

00

170

00

122

00

v2162

00

221

00

170

00

122

00

v3162

00

221

00

170

00

122

00

v4162

00

220

10

170

00

122

00

v5162

00

221

00

170

00

122

00

v6162

00

221

00

170

00

121

10

Total

1944

00

2651

10

2040

00

1461

30

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

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Diabetes ResearchJournal of

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Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 4: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

4 Computational and Mathematical Methods in Medicine

(a) (b)

Figure 3 Simulation result Time and number of peaks detection in each lead are shown ((a) record s0292lrem (b) record s0291lrem)

Sample

005

48000460004400042000minus1

minus05

Am

plitu

def[n

]

(a)

Sample

31

48000460004400042000minus1

Am

plitu

des[n]

(b)

Sample

20

48000460004400042000minus2

Am

plitu

dee[n]

(c)

Am

plitu

de

Sample

0

48000460004400042000minus2

(d)

Figure 4 Process of preparations of ECG signal (record s0291lrem lead v3)

fact samples with greater amplitude than the threshold areselected as output

threshold = 10038161003816100381610038161003816120581120583 (1 minus 1205902)10038161003816100381610038161003816 if 120590 lt 120583

threshold = 10038161003816100381610038161003816120581120590 (1 minus 1205832)10038161003816100381610038161003816 if 120590 gt 120583(9)

where 120581 is a constant

3 Result

The experimental results are obtained after simulation on 70healthy patientsrsquo signals for all 12 leads and using PTB Diag-nostic ECGDatabase (PTBDB)The Physikalisch-TechnischeBundesanstalt (PTB) is the National Metrology Institute ofGermany PTB database is provided for PhysioNet and hasdifferent morphologies The ECGs in this database obtain 15input channels including the conventional 12 leads (i ii iiiavr avl avf v1 v2 v3 v4 v5 and v6) together with the 3

Frank lead ECGs (vx vy and vz) Input voltage is plusmn16mVinput resistance is 100Ω ADC resolution is equal to 16 bitswith 05120583LSB and sampling frequency is equal to 1 KHz[20 21]The proposed algorithmwas performed on a 24GHzIntel core i3 CPU using GNU Octave version 402 [29] Aselected signal from patient 117 has a variety of physiologyand baseline drift Leads (i ii avl avf v3 v4 v5 and v6) ofrecord s0291lrem and leads (i ii iii avf v1 v2 v4 v5 and v6)of record s0292lrem have high amplitude Leads (i avl v2 v3and v4) of record s0291lrem and leads (avr avl and avf) ofrecord s0292lrem have a sharp and tall T-wave

Figure 3 shows the result of simulation to detect each leadof patient 117 in Octave Figures 4 and 5 show the process ofECG signal provision and peak detectionThe QRS detectionof the 12 channels of healthy ECG signal in patient 117 ofthe PTB database is reported in Table 1 and the AppendixDetection of the 12 leads is shown in Figure 6 Figure 7 shows3 leads of 3 cases

Computational and Mathematical Methods in Medicine 5

Table 1 The QRS detection of ECG signal of the PTB database

Case TP FN FP DER Se +P Accs0010 rem 624 0 0 0000 100000 100000 100000s0014lrem 1987 0 0 0000 100000 100000 100000s0015lrem 1815 0 2 0110 100000 99890 99890s0017lrem 1673 0 5 0299 100000 99702 99702s0020arem 1906 4 21 1312 99791 98910 98705s0020brem 1867 5 20 1339 99733 98940 98679s0021arem 2207 1 0 0045 99955 100000 99955s0021brem 2196 0 0 0000 100000 100000 100000s0025lrem 2382 6 0 0252 99749 100000 99749s0029lrem 1638 0 0 0000 100000 100000 100000s0031lrem 2111 1 0 0047 99953 100000 99953s0035 rem 552 0 0 0000 100000 100000 100000s0036lrem 2066 0 2 0097 100000 99903 99903s0037lrem 1479 0 3 0203 100000 99798 99798s0038lrem 1572 0 0 0000 100000 100000 100000s0039lrem 2088 0 0 0000 100000 100000 100000s0042lrem 1815 0 0 0000 100000 100000 100000s0043lrem 1212 0 0 0000 100000 100000 100000s0044lrem 1812 0 0 0000 100000 100000 100000s0045lrem 1968 0 0 0000 100000 100000 100000s0046lrem 1944 0 0 0000 100000 100000 100000s0047lrem 2651 1 0 0038 99962 100000 99962s0049lrem 2040 0 0 0000 100000 100000 100000s0050lrem 1461 3 0 0205 99795 100000 99795s0051lrem 1912 0 2 0105 100000 99896 99896s0052lrem 1356 0 0 0000 100000 100000 100000s0053lrem 2148 0 0 0000 100000 100000 100000s0054lrem 1979 31 2 1668 98458 99899 98360s0055lrem 1381 0 1 0072 100000 99928 99928s0056lrem 1732 0 0 0000 100000 100000 100000s0057lrem 1896 0 0 0000 100000 100000 100000s0058lrem 2017 0 1 0050 100000 99950 99950s0059lrem 1800 0 0 0000 100000 100000 100000s0060lrem 140 0 0 0000 100000 100000 100000s0062lrem 1488 0 0 0000 100000 100000 100000s0063lrem 1845 3 0 0163 99838 100000 99838s0064lrem 1797 3 0 0167 99833 100000 99833s0065lrem 1704 0 0 0000 100000 100000 100000s0066lrem 1513 0 1 0066 100000 99934 99934s0067lrem 424 0 4 0943 100000 99065 99065s0068lrem 1377 5 15 1452 99638 98922 98568s0069lrem 1188 0 0 0000 100000 100000 100000s0070lrem 1983 0 1 0050 100000 99950 99950s0071lrem 1848 0 0 0000 100000 100000 100000s0072lrem 2040 0 0 0000 100000 100000 100000s0073lrem 2125 5 0 0235 99765 100000 99765s0074lrem 1140 0 0 0000 100000 100000 100000s0075lrem 1453 0 1 0069 100000 99931 99931s0076lrem 1308 0 0 0000 100000 100000 100000s0077lrem 1692 0 0 0000 100000 100000 100000s0078lrem 1225 0 1 0082 100000 99918 99918

6 Computational and Mathematical Methods in Medicine

Table 1 Continued

Case TP FN FP DER Se +P Accs0079lrem 1620 0 0 0000 100000 100000 100000s0080lrem 1556 0 0 0000 100000 100000 100000s0082lrem 1602 0 0 0000 100000 100000 100000s0083lrem 1465 1 0 0068 99932 100000 99932s0084lrem 1464 0 0 0000 100000 100000 100000s0085lrem 1276 0 4 0313 100000 99688 99688s0097lrem 2133 0 1 0047 100000 99953 99953s0101lrem 1500 0 0 0000 100000 100000 100000s0103lrem 1273 0 2 0157 100000 99843 99843s0149lrem 1572 0 0 0000 100000 100000 100000s0152lrem 1532 4 0 0261 99740 100000 99740s0087lrem 1654 12 0 0726 99280 100000 99280s0088lrem 1728 0 0 0000 100000 100000 100000s0091lrem 1380 1 1 0145 99928 99928 99855s0095lrem 1797 3 0 0167 99833 100000 99833s0096lrem 2603 1 0 0038 99962 100000 99962s0150lrem 1583 1 0 0063 99937 100000 99937s0090lrem 1358 0 2 0147 100000 99853 99853s0093lrem 1249 0 1 0080 100000 99920 99920s0291lrem 1548 0 0 0000 100000 100000 100000s0292lrem 1584 0 0 0000 100000 100000 100000Total 119054 91 93 0155 99924 99922 99846

0

43000420004100040000390003800037000Sample

Am

plitu

def[n

]

minus1

(a)

31

43000420004100040000390003800037000Sample

Am

plitu

des[n]

minus1

(b)

1

43000420004100040000390003800037000Sample

Am

plitu

dee[n]

minus1

(c)

Am

plitu

de 02

43000420004100040000390003800037000Sample

minus02minus06minus1

(d)

Figure 5 Process of preparation of ECG signal (record s0292lrem lead avr)

In order to define performance and efficiency of the algo-rithm the ClassificationAccuracy (Acc) Positive Predictivity(+P) sensitivity (Se) and detection error rate were calculatedby using the following equations

Acc = TPTP + FN + FP

times 100

+P = TPTP + FP

times 100

Se = TPTP + FN

times 100

DER = FP + FNTP

times 100

(10)

Here TP defines a true detected peak by the algorithmFN (false negative) is the number of not detected R peaksand FP (false positive) is the number of noise spikes detectedas R peaks [3 30]

Figures 4(a) and 5(a) show the output after the band-pass filter 119891[119899] and normalized amplitude 119886[119899] Figures4(b) and 5(b) show Shannon energy 119904[119899] and normalizedamplitude and Figures 4(c) and 5(c) show after envelope119890[119899] signal QRS complex of ECG signal is shown inFigures 4(d) and 5(d) Red line defines a detected peak 119910-axis represents the amplitude and 119909-axis represents thesample

In this study the proposed technique is tested on 840leads of PTB Diagnostic ECGDatabase (PTBDB) and values

Computational and Mathematical Methods in Medicine 7

v5

210

440004200040000380003600034000

avl 02

0

440004200040000380003600034000minus02

avf 05

0

440004200040000380003600034000minus05

avr

440004200040000380003600034000

minus06minus1

minus0202

i

060402

0

440004200040000380003600034000

minus02minus04

v1

0

440004200040000380003600034000

04

minus04minus08

v2

10

440004200040000380003600034000minus2minus1

v4440004200040000380003600034000

minus15

05

minus05

115

0

440004200040000380003600034000

av6

05

minus05

v3

10

440004200040000380003600034000minus2minus1iii

10602

440004200040000380003600034000minus02

ii

105

0

440004200040000380003600034000minus05

Figure 6 Detected QRS complex of ECG data (record s0292lrem) red line defines QRS complex detection 119910-axis represents the amplitudeand 119909-axis represents the sample

Am

plitu

de

0

3400032000300002800026000240002200020000

FPminus05

05

(a)

Am

plitu

de

0

140001200010000800060004000

FN

minus05

05

minus1

(b)

Am

plitu

de

1

0

4800046000440004200040000

FN

minus05

05

(c)

Figure 7 (a) Detected QRS complex of ECG data (record s0020arem lead avf) Records s0020arem and s0020brem include tall and sharpP-wave and T-wave In this case the QRS area has low energy (b) Detected QRS complex of s0087lrem-lead 3 This case includes IrregularRR interval (c) Lead v5 of s0089lrem FN (false negative) is the number of not detected R peaks and FP (false positive) is the number ofnoise spikes detected as R peaks 119910-axis represents the amplitude and 119909-axis represents the sample

8 Computational and Mathematical Methods in MedicineTa

ble2Th

eQRS

detectionof

the12channelsof

theP

TBdatabase

(a)

Leads

s0010rem

s0014lrem

s0015lrem

s0017lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i52

00

166

00

151

00

139

00

ii52

00

165

00

151

00

142

03

iii52

00

165

00

152

01

139

00

avr

520

0166

00

152

00

139

00

avl

520

0166

00

152

01

139

00

avf

520

0165

00

151

00

140

01

v152

00

165

00

151

00

140

01

v252

00

166

00

151

00

139

00

v352

00

166

00

151

00

139

00

v452

00

166

00

151

00

139

00

v552

00

166

00

151

00

139

00

v652

00

165

00

151

00

139

00

Total

624

00

1987

00

1815

02

1673

05

(b)

Leads

s0020arem

s0020brem

s0021arem

s0021brem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

156

00

184

00

183

00

ii159

00

156

00

184

00

183

00

iii159

00

156

00

184

00

183

00

avr

159

00

156

00

184

00

183

00

avl

159

00

156

00

184

00

183

00

avf

158

321

151

520

184

00

183

00

v1159

00

156

00

184

00

183

00

v2159

00

156

00

184

00

183

00

v3159

00

156

00

183

10

183

00

v4158

10

156

00

184

00

183

00

v5159

00

156

00

184

00

183

00

v6159

00

156

00

184

00

183

00

Total

1906

421

1867

520

2207

10

2196

00

(c)

Leads

s0025lrem

s0029lrem

s0031lrem

s0035rem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i199

00

136

00

176

00

460

0ii

199

00

137

00

176

00

460

0iii

197

20

137

00

176

00

460

0avr

197

20

137

00

176

00

460

0avl

197

20

136

00

175

10

460

0avf

199

00

137

00

176

00

460

0v1

199

00

136

00

176

00

460

0v2

199

00

136

00

176

00

460

0v3

199

00

137

00

176

00

460

0v4

199

00

136

00

176

00

460

0v5

199

00

137

00

176

00

460

0v6

199

00

136

00

176

00

460

0Total

2382

60

1638

00

2111

10

552

00

Computational and Mathematical Methods in Medicine 9(d)

Leads

s0036lrem

s0037lrem

s0038lrem

s0039lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i173

01

123

00

131

00

174

00

ii172

00

123

00

131

00

174

00

iii172

00

124

01

131

00

174

00

avr

173

01

123

00

131

00

174

00

avl

172

00

124

01

131

00

174

00

avf

172

00

124

01

131

00

174

00

v1172

00

123

00

131

00

174

00

v2172

00

123

00

131

00

174

00

v3172

00

123

00

131

00

174

00

v4172

00

123

00

131

00

174

00

v5172

00

123

00

131

00

174

00

v6172

00

123

00

131

00

174

00

Total

2066

02

1479

03

1572

00

2088

00

(e)

Leads

s004

2lrem

s0043lrem

s004

4lrem

s0045lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i152

00

101

00

151

00

164

00

ii151

00

101

00

151

00

164

00

iii151

00

101

00

151

00

164

00

avr

152

00

101

00

151

00

164

00

avl

152

00

101

00

151

00

164

00

avf

151

00

101

00

151

00

164

00

v1151

00

101

00

151

00

164

00

v2151

00

101

00

151

00

164

00

v3151

00

101

00

151

00

164

00

v4151

00

101

00

151

00

164

00

v5151

00

101

00

151

00

164

00

v6151

00

101

00

151

00

164

00

Total

1815

00

1212

00

1812

00

1968

00

(f)

Leads

s004

6lrem

s0047lrem

s0049lrem

s0050lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i162

00

221

00

170

00

121

10

ii162

00

221

00

170

00

121

10

iii162

00

221

00

170

00

122

00

avr

162

00

221

00

170

00

122

00

avl

162

00

221

00

170

00

122

00

avf

162

00

221

00

170

00

122

00

v1162

00

221

00

170

00

122

00

v2162

00

221

00

170

00

122

00

v3162

00

221

00

170

00

122

00

v4162

00

220

10

170

00

122

00

v5162

00

221

00

170

00

122

00

v6162

00

221

00

170

00

121

10

Total

1944

00

2651

10

2040

00

1461

30

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

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Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 5: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

Computational and Mathematical Methods in Medicine 5

Table 1 The QRS detection of ECG signal of the PTB database

Case TP FN FP DER Se +P Accs0010 rem 624 0 0 0000 100000 100000 100000s0014lrem 1987 0 0 0000 100000 100000 100000s0015lrem 1815 0 2 0110 100000 99890 99890s0017lrem 1673 0 5 0299 100000 99702 99702s0020arem 1906 4 21 1312 99791 98910 98705s0020brem 1867 5 20 1339 99733 98940 98679s0021arem 2207 1 0 0045 99955 100000 99955s0021brem 2196 0 0 0000 100000 100000 100000s0025lrem 2382 6 0 0252 99749 100000 99749s0029lrem 1638 0 0 0000 100000 100000 100000s0031lrem 2111 1 0 0047 99953 100000 99953s0035 rem 552 0 0 0000 100000 100000 100000s0036lrem 2066 0 2 0097 100000 99903 99903s0037lrem 1479 0 3 0203 100000 99798 99798s0038lrem 1572 0 0 0000 100000 100000 100000s0039lrem 2088 0 0 0000 100000 100000 100000s0042lrem 1815 0 0 0000 100000 100000 100000s0043lrem 1212 0 0 0000 100000 100000 100000s0044lrem 1812 0 0 0000 100000 100000 100000s0045lrem 1968 0 0 0000 100000 100000 100000s0046lrem 1944 0 0 0000 100000 100000 100000s0047lrem 2651 1 0 0038 99962 100000 99962s0049lrem 2040 0 0 0000 100000 100000 100000s0050lrem 1461 3 0 0205 99795 100000 99795s0051lrem 1912 0 2 0105 100000 99896 99896s0052lrem 1356 0 0 0000 100000 100000 100000s0053lrem 2148 0 0 0000 100000 100000 100000s0054lrem 1979 31 2 1668 98458 99899 98360s0055lrem 1381 0 1 0072 100000 99928 99928s0056lrem 1732 0 0 0000 100000 100000 100000s0057lrem 1896 0 0 0000 100000 100000 100000s0058lrem 2017 0 1 0050 100000 99950 99950s0059lrem 1800 0 0 0000 100000 100000 100000s0060lrem 140 0 0 0000 100000 100000 100000s0062lrem 1488 0 0 0000 100000 100000 100000s0063lrem 1845 3 0 0163 99838 100000 99838s0064lrem 1797 3 0 0167 99833 100000 99833s0065lrem 1704 0 0 0000 100000 100000 100000s0066lrem 1513 0 1 0066 100000 99934 99934s0067lrem 424 0 4 0943 100000 99065 99065s0068lrem 1377 5 15 1452 99638 98922 98568s0069lrem 1188 0 0 0000 100000 100000 100000s0070lrem 1983 0 1 0050 100000 99950 99950s0071lrem 1848 0 0 0000 100000 100000 100000s0072lrem 2040 0 0 0000 100000 100000 100000s0073lrem 2125 5 0 0235 99765 100000 99765s0074lrem 1140 0 0 0000 100000 100000 100000s0075lrem 1453 0 1 0069 100000 99931 99931s0076lrem 1308 0 0 0000 100000 100000 100000s0077lrem 1692 0 0 0000 100000 100000 100000s0078lrem 1225 0 1 0082 100000 99918 99918

6 Computational and Mathematical Methods in Medicine

Table 1 Continued

Case TP FN FP DER Se +P Accs0079lrem 1620 0 0 0000 100000 100000 100000s0080lrem 1556 0 0 0000 100000 100000 100000s0082lrem 1602 0 0 0000 100000 100000 100000s0083lrem 1465 1 0 0068 99932 100000 99932s0084lrem 1464 0 0 0000 100000 100000 100000s0085lrem 1276 0 4 0313 100000 99688 99688s0097lrem 2133 0 1 0047 100000 99953 99953s0101lrem 1500 0 0 0000 100000 100000 100000s0103lrem 1273 0 2 0157 100000 99843 99843s0149lrem 1572 0 0 0000 100000 100000 100000s0152lrem 1532 4 0 0261 99740 100000 99740s0087lrem 1654 12 0 0726 99280 100000 99280s0088lrem 1728 0 0 0000 100000 100000 100000s0091lrem 1380 1 1 0145 99928 99928 99855s0095lrem 1797 3 0 0167 99833 100000 99833s0096lrem 2603 1 0 0038 99962 100000 99962s0150lrem 1583 1 0 0063 99937 100000 99937s0090lrem 1358 0 2 0147 100000 99853 99853s0093lrem 1249 0 1 0080 100000 99920 99920s0291lrem 1548 0 0 0000 100000 100000 100000s0292lrem 1584 0 0 0000 100000 100000 100000Total 119054 91 93 0155 99924 99922 99846

0

43000420004100040000390003800037000Sample

Am

plitu

def[n

]

minus1

(a)

31

43000420004100040000390003800037000Sample

Am

plitu

des[n]

minus1

(b)

1

43000420004100040000390003800037000Sample

Am

plitu

dee[n]

minus1

(c)

Am

plitu

de 02

43000420004100040000390003800037000Sample

minus02minus06minus1

(d)

Figure 5 Process of preparation of ECG signal (record s0292lrem lead avr)

In order to define performance and efficiency of the algo-rithm the ClassificationAccuracy (Acc) Positive Predictivity(+P) sensitivity (Se) and detection error rate were calculatedby using the following equations

Acc = TPTP + FN + FP

times 100

+P = TPTP + FP

times 100

Se = TPTP + FN

times 100

DER = FP + FNTP

times 100

(10)

Here TP defines a true detected peak by the algorithmFN (false negative) is the number of not detected R peaksand FP (false positive) is the number of noise spikes detectedas R peaks [3 30]

Figures 4(a) and 5(a) show the output after the band-pass filter 119891[119899] and normalized amplitude 119886[119899] Figures4(b) and 5(b) show Shannon energy 119904[119899] and normalizedamplitude and Figures 4(c) and 5(c) show after envelope119890[119899] signal QRS complex of ECG signal is shown inFigures 4(d) and 5(d) Red line defines a detected peak 119910-axis represents the amplitude and 119909-axis represents thesample

In this study the proposed technique is tested on 840leads of PTB Diagnostic ECGDatabase (PTBDB) and values

Computational and Mathematical Methods in Medicine 7

v5

210

440004200040000380003600034000

avl 02

0

440004200040000380003600034000minus02

avf 05

0

440004200040000380003600034000minus05

avr

440004200040000380003600034000

minus06minus1

minus0202

i

060402

0

440004200040000380003600034000

minus02minus04

v1

0

440004200040000380003600034000

04

minus04minus08

v2

10

440004200040000380003600034000minus2minus1

v4440004200040000380003600034000

minus15

05

minus05

115

0

440004200040000380003600034000

av6

05

minus05

v3

10

440004200040000380003600034000minus2minus1iii

10602

440004200040000380003600034000minus02

ii

105

0

440004200040000380003600034000minus05

Figure 6 Detected QRS complex of ECG data (record s0292lrem) red line defines QRS complex detection 119910-axis represents the amplitudeand 119909-axis represents the sample

Am

plitu

de

0

3400032000300002800026000240002200020000

FPminus05

05

(a)

Am

plitu

de

0

140001200010000800060004000

FN

minus05

05

minus1

(b)

Am

plitu

de

1

0

4800046000440004200040000

FN

minus05

05

(c)

Figure 7 (a) Detected QRS complex of ECG data (record s0020arem lead avf) Records s0020arem and s0020brem include tall and sharpP-wave and T-wave In this case the QRS area has low energy (b) Detected QRS complex of s0087lrem-lead 3 This case includes IrregularRR interval (c) Lead v5 of s0089lrem FN (false negative) is the number of not detected R peaks and FP (false positive) is the number ofnoise spikes detected as R peaks 119910-axis represents the amplitude and 119909-axis represents the sample

8 Computational and Mathematical Methods in MedicineTa

ble2Th

eQRS

detectionof

the12channelsof

theP

TBdatabase

(a)

Leads

s0010rem

s0014lrem

s0015lrem

s0017lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i52

00

166

00

151

00

139

00

ii52

00

165

00

151

00

142

03

iii52

00

165

00

152

01

139

00

avr

520

0166

00

152

00

139

00

avl

520

0166

00

152

01

139

00

avf

520

0165

00

151

00

140

01

v152

00

165

00

151

00

140

01

v252

00

166

00

151

00

139

00

v352

00

166

00

151

00

139

00

v452

00

166

00

151

00

139

00

v552

00

166

00

151

00

139

00

v652

00

165

00

151

00

139

00

Total

624

00

1987

00

1815

02

1673

05

(b)

Leads

s0020arem

s0020brem

s0021arem

s0021brem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

156

00

184

00

183

00

ii159

00

156

00

184

00

183

00

iii159

00

156

00

184

00

183

00

avr

159

00

156

00

184

00

183

00

avl

159

00

156

00

184

00

183

00

avf

158

321

151

520

184

00

183

00

v1159

00

156

00

184

00

183

00

v2159

00

156

00

184

00

183

00

v3159

00

156

00

183

10

183

00

v4158

10

156

00

184

00

183

00

v5159

00

156

00

184

00

183

00

v6159

00

156

00

184

00

183

00

Total

1906

421

1867

520

2207

10

2196

00

(c)

Leads

s0025lrem

s0029lrem

s0031lrem

s0035rem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i199

00

136

00

176

00

460

0ii

199

00

137

00

176

00

460

0iii

197

20

137

00

176

00

460

0avr

197

20

137

00

176

00

460

0avl

197

20

136

00

175

10

460

0avf

199

00

137

00

176

00

460

0v1

199

00

136

00

176

00

460

0v2

199

00

136

00

176

00

460

0v3

199

00

137

00

176

00

460

0v4

199

00

136

00

176

00

460

0v5

199

00

137

00

176

00

460

0v6

199

00

136

00

176

00

460

0Total

2382

60

1638

00

2111

10

552

00

Computational and Mathematical Methods in Medicine 9(d)

Leads

s0036lrem

s0037lrem

s0038lrem

s0039lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i173

01

123

00

131

00

174

00

ii172

00

123

00

131

00

174

00

iii172

00

124

01

131

00

174

00

avr

173

01

123

00

131

00

174

00

avl

172

00

124

01

131

00

174

00

avf

172

00

124

01

131

00

174

00

v1172

00

123

00

131

00

174

00

v2172

00

123

00

131

00

174

00

v3172

00

123

00

131

00

174

00

v4172

00

123

00

131

00

174

00

v5172

00

123

00

131

00

174

00

v6172

00

123

00

131

00

174

00

Total

2066

02

1479

03

1572

00

2088

00

(e)

Leads

s004

2lrem

s0043lrem

s004

4lrem

s0045lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i152

00

101

00

151

00

164

00

ii151

00

101

00

151

00

164

00

iii151

00

101

00

151

00

164

00

avr

152

00

101

00

151

00

164

00

avl

152

00

101

00

151

00

164

00

avf

151

00

101

00

151

00

164

00

v1151

00

101

00

151

00

164

00

v2151

00

101

00

151

00

164

00

v3151

00

101

00

151

00

164

00

v4151

00

101

00

151

00

164

00

v5151

00

101

00

151

00

164

00

v6151

00

101

00

151

00

164

00

Total

1815

00

1212

00

1812

00

1968

00

(f)

Leads

s004

6lrem

s0047lrem

s0049lrem

s0050lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i162

00

221

00

170

00

121

10

ii162

00

221

00

170

00

121

10

iii162

00

221

00

170

00

122

00

avr

162

00

221

00

170

00

122

00

avl

162

00

221

00

170

00

122

00

avf

162

00

221

00

170

00

122

00

v1162

00

221

00

170

00

122

00

v2162

00

221

00

170

00

122

00

v3162

00

221

00

170

00

122

00

v4162

00

220

10

170

00

122

00

v5162

00

221

00

170

00

122

00

v6162

00

221

00

170

00

121

10

Total

1944

00

2651

10

2040

00

1461

30

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

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OncologyJournal of

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Oxidative Medicine and Cellular Longevity

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PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Computational and Mathematical Methods in Medicine

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Research and TreatmentAIDS

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Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 6: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

6 Computational and Mathematical Methods in Medicine

Table 1 Continued

Case TP FN FP DER Se +P Accs0079lrem 1620 0 0 0000 100000 100000 100000s0080lrem 1556 0 0 0000 100000 100000 100000s0082lrem 1602 0 0 0000 100000 100000 100000s0083lrem 1465 1 0 0068 99932 100000 99932s0084lrem 1464 0 0 0000 100000 100000 100000s0085lrem 1276 0 4 0313 100000 99688 99688s0097lrem 2133 0 1 0047 100000 99953 99953s0101lrem 1500 0 0 0000 100000 100000 100000s0103lrem 1273 0 2 0157 100000 99843 99843s0149lrem 1572 0 0 0000 100000 100000 100000s0152lrem 1532 4 0 0261 99740 100000 99740s0087lrem 1654 12 0 0726 99280 100000 99280s0088lrem 1728 0 0 0000 100000 100000 100000s0091lrem 1380 1 1 0145 99928 99928 99855s0095lrem 1797 3 0 0167 99833 100000 99833s0096lrem 2603 1 0 0038 99962 100000 99962s0150lrem 1583 1 0 0063 99937 100000 99937s0090lrem 1358 0 2 0147 100000 99853 99853s0093lrem 1249 0 1 0080 100000 99920 99920s0291lrem 1548 0 0 0000 100000 100000 100000s0292lrem 1584 0 0 0000 100000 100000 100000Total 119054 91 93 0155 99924 99922 99846

0

43000420004100040000390003800037000Sample

Am

plitu

def[n

]

minus1

(a)

31

43000420004100040000390003800037000Sample

Am

plitu

des[n]

minus1

(b)

1

43000420004100040000390003800037000Sample

Am

plitu

dee[n]

minus1

(c)

Am

plitu

de 02

43000420004100040000390003800037000Sample

minus02minus06minus1

(d)

Figure 5 Process of preparation of ECG signal (record s0292lrem lead avr)

In order to define performance and efficiency of the algo-rithm the ClassificationAccuracy (Acc) Positive Predictivity(+P) sensitivity (Se) and detection error rate were calculatedby using the following equations

Acc = TPTP + FN + FP

times 100

+P = TPTP + FP

times 100

Se = TPTP + FN

times 100

DER = FP + FNTP

times 100

(10)

Here TP defines a true detected peak by the algorithmFN (false negative) is the number of not detected R peaksand FP (false positive) is the number of noise spikes detectedas R peaks [3 30]

Figures 4(a) and 5(a) show the output after the band-pass filter 119891[119899] and normalized amplitude 119886[119899] Figures4(b) and 5(b) show Shannon energy 119904[119899] and normalizedamplitude and Figures 4(c) and 5(c) show after envelope119890[119899] signal QRS complex of ECG signal is shown inFigures 4(d) and 5(d) Red line defines a detected peak 119910-axis represents the amplitude and 119909-axis represents thesample

In this study the proposed technique is tested on 840leads of PTB Diagnostic ECGDatabase (PTBDB) and values

Computational and Mathematical Methods in Medicine 7

v5

210

440004200040000380003600034000

avl 02

0

440004200040000380003600034000minus02

avf 05

0

440004200040000380003600034000minus05

avr

440004200040000380003600034000

minus06minus1

minus0202

i

060402

0

440004200040000380003600034000

minus02minus04

v1

0

440004200040000380003600034000

04

minus04minus08

v2

10

440004200040000380003600034000minus2minus1

v4440004200040000380003600034000

minus15

05

minus05

115

0

440004200040000380003600034000

av6

05

minus05

v3

10

440004200040000380003600034000minus2minus1iii

10602

440004200040000380003600034000minus02

ii

105

0

440004200040000380003600034000minus05

Figure 6 Detected QRS complex of ECG data (record s0292lrem) red line defines QRS complex detection 119910-axis represents the amplitudeand 119909-axis represents the sample

Am

plitu

de

0

3400032000300002800026000240002200020000

FPminus05

05

(a)

Am

plitu

de

0

140001200010000800060004000

FN

minus05

05

minus1

(b)

Am

plitu

de

1

0

4800046000440004200040000

FN

minus05

05

(c)

Figure 7 (a) Detected QRS complex of ECG data (record s0020arem lead avf) Records s0020arem and s0020brem include tall and sharpP-wave and T-wave In this case the QRS area has low energy (b) Detected QRS complex of s0087lrem-lead 3 This case includes IrregularRR interval (c) Lead v5 of s0089lrem FN (false negative) is the number of not detected R peaks and FP (false positive) is the number ofnoise spikes detected as R peaks 119910-axis represents the amplitude and 119909-axis represents the sample

8 Computational and Mathematical Methods in MedicineTa

ble2Th

eQRS

detectionof

the12channelsof

theP

TBdatabase

(a)

Leads

s0010rem

s0014lrem

s0015lrem

s0017lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i52

00

166

00

151

00

139

00

ii52

00

165

00

151

00

142

03

iii52

00

165

00

152

01

139

00

avr

520

0166

00

152

00

139

00

avl

520

0166

00

152

01

139

00

avf

520

0165

00

151

00

140

01

v152

00

165

00

151

00

140

01

v252

00

166

00

151

00

139

00

v352

00

166

00

151

00

139

00

v452

00

166

00

151

00

139

00

v552

00

166

00

151

00

139

00

v652

00

165

00

151

00

139

00

Total

624

00

1987

00

1815

02

1673

05

(b)

Leads

s0020arem

s0020brem

s0021arem

s0021brem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

156

00

184

00

183

00

ii159

00

156

00

184

00

183

00

iii159

00

156

00

184

00

183

00

avr

159

00

156

00

184

00

183

00

avl

159

00

156

00

184

00

183

00

avf

158

321

151

520

184

00

183

00

v1159

00

156

00

184

00

183

00

v2159

00

156

00

184

00

183

00

v3159

00

156

00

183

10

183

00

v4158

10

156

00

184

00

183

00

v5159

00

156

00

184

00

183

00

v6159

00

156

00

184

00

183

00

Total

1906

421

1867

520

2207

10

2196

00

(c)

Leads

s0025lrem

s0029lrem

s0031lrem

s0035rem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i199

00

136

00

176

00

460

0ii

199

00

137

00

176

00

460

0iii

197

20

137

00

176

00

460

0avr

197

20

137

00

176

00

460

0avl

197

20

136

00

175

10

460

0avf

199

00

137

00

176

00

460

0v1

199

00

136

00

176

00

460

0v2

199

00

136

00

176

00

460

0v3

199

00

137

00

176

00

460

0v4

199

00

136

00

176

00

460

0v5

199

00

137

00

176

00

460

0v6

199

00

136

00

176

00

460

0Total

2382

60

1638

00

2111

10

552

00

Computational and Mathematical Methods in Medicine 9(d)

Leads

s0036lrem

s0037lrem

s0038lrem

s0039lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i173

01

123

00

131

00

174

00

ii172

00

123

00

131

00

174

00

iii172

00

124

01

131

00

174

00

avr

173

01

123

00

131

00

174

00

avl

172

00

124

01

131

00

174

00

avf

172

00

124

01

131

00

174

00

v1172

00

123

00

131

00

174

00

v2172

00

123

00

131

00

174

00

v3172

00

123

00

131

00

174

00

v4172

00

123

00

131

00

174

00

v5172

00

123

00

131

00

174

00

v6172

00

123

00

131

00

174

00

Total

2066

02

1479

03

1572

00

2088

00

(e)

Leads

s004

2lrem

s0043lrem

s004

4lrem

s0045lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i152

00

101

00

151

00

164

00

ii151

00

101

00

151

00

164

00

iii151

00

101

00

151

00

164

00

avr

152

00

101

00

151

00

164

00

avl

152

00

101

00

151

00

164

00

avf

151

00

101

00

151

00

164

00

v1151

00

101

00

151

00

164

00

v2151

00

101

00

151

00

164

00

v3151

00

101

00

151

00

164

00

v4151

00

101

00

151

00

164

00

v5151

00

101

00

151

00

164

00

v6151

00

101

00

151

00

164

00

Total

1815

00

1212

00

1812

00

1968

00

(f)

Leads

s004

6lrem

s0047lrem

s0049lrem

s0050lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i162

00

221

00

170

00

121

10

ii162

00

221

00

170

00

121

10

iii162

00

221

00

170

00

122

00

avr

162

00

221

00

170

00

122

00

avl

162

00

221

00

170

00

122

00

avf

162

00

221

00

170

00

122

00

v1162

00

221

00

170

00

122

00

v2162

00

221

00

170

00

122

00

v3162

00

221

00

170

00

122

00

v4162

00

220

10

170

00

122

00

v5162

00

221

00

170

00

122

00

v6162

00

221

00

170

00

121

10

Total

1944

00

2651

10

2040

00

1461

30

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

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Oxidative Medicine and Cellular Longevity

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PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

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Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

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Research and TreatmentAIDS

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Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 7: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

Computational and Mathematical Methods in Medicine 7

v5

210

440004200040000380003600034000

avl 02

0

440004200040000380003600034000minus02

avf 05

0

440004200040000380003600034000minus05

avr

440004200040000380003600034000

minus06minus1

minus0202

i

060402

0

440004200040000380003600034000

minus02minus04

v1

0

440004200040000380003600034000

04

minus04minus08

v2

10

440004200040000380003600034000minus2minus1

v4440004200040000380003600034000

minus15

05

minus05

115

0

440004200040000380003600034000

av6

05

minus05

v3

10

440004200040000380003600034000minus2minus1iii

10602

440004200040000380003600034000minus02

ii

105

0

440004200040000380003600034000minus05

Figure 6 Detected QRS complex of ECG data (record s0292lrem) red line defines QRS complex detection 119910-axis represents the amplitudeand 119909-axis represents the sample

Am

plitu

de

0

3400032000300002800026000240002200020000

FPminus05

05

(a)

Am

plitu

de

0

140001200010000800060004000

FN

minus05

05

minus1

(b)

Am

plitu

de

1

0

4800046000440004200040000

FN

minus05

05

(c)

Figure 7 (a) Detected QRS complex of ECG data (record s0020arem lead avf) Records s0020arem and s0020brem include tall and sharpP-wave and T-wave In this case the QRS area has low energy (b) Detected QRS complex of s0087lrem-lead 3 This case includes IrregularRR interval (c) Lead v5 of s0089lrem FN (false negative) is the number of not detected R peaks and FP (false positive) is the number ofnoise spikes detected as R peaks 119910-axis represents the amplitude and 119909-axis represents the sample

8 Computational and Mathematical Methods in MedicineTa

ble2Th

eQRS

detectionof

the12channelsof

theP

TBdatabase

(a)

Leads

s0010rem

s0014lrem

s0015lrem

s0017lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i52

00

166

00

151

00

139

00

ii52

00

165

00

151

00

142

03

iii52

00

165

00

152

01

139

00

avr

520

0166

00

152

00

139

00

avl

520

0166

00

152

01

139

00

avf

520

0165

00

151

00

140

01

v152

00

165

00

151

00

140

01

v252

00

166

00

151

00

139

00

v352

00

166

00

151

00

139

00

v452

00

166

00

151

00

139

00

v552

00

166

00

151

00

139

00

v652

00

165

00

151

00

139

00

Total

624

00

1987

00

1815

02

1673

05

(b)

Leads

s0020arem

s0020brem

s0021arem

s0021brem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

156

00

184

00

183

00

ii159

00

156

00

184

00

183

00

iii159

00

156

00

184

00

183

00

avr

159

00

156

00

184

00

183

00

avl

159

00

156

00

184

00

183

00

avf

158

321

151

520

184

00

183

00

v1159

00

156

00

184

00

183

00

v2159

00

156

00

184

00

183

00

v3159

00

156

00

183

10

183

00

v4158

10

156

00

184

00

183

00

v5159

00

156

00

184

00

183

00

v6159

00

156

00

184

00

183

00

Total

1906

421

1867

520

2207

10

2196

00

(c)

Leads

s0025lrem

s0029lrem

s0031lrem

s0035rem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i199

00

136

00

176

00

460

0ii

199

00

137

00

176

00

460

0iii

197

20

137

00

176

00

460

0avr

197

20

137

00

176

00

460

0avl

197

20

136

00

175

10

460

0avf

199

00

137

00

176

00

460

0v1

199

00

136

00

176

00

460

0v2

199

00

136

00

176

00

460

0v3

199

00

137

00

176

00

460

0v4

199

00

136

00

176

00

460

0v5

199

00

137

00

176

00

460

0v6

199

00

136

00

176

00

460

0Total

2382

60

1638

00

2111

10

552

00

Computational and Mathematical Methods in Medicine 9(d)

Leads

s0036lrem

s0037lrem

s0038lrem

s0039lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i173

01

123

00

131

00

174

00

ii172

00

123

00

131

00

174

00

iii172

00

124

01

131

00

174

00

avr

173

01

123

00

131

00

174

00

avl

172

00

124

01

131

00

174

00

avf

172

00

124

01

131

00

174

00

v1172

00

123

00

131

00

174

00

v2172

00

123

00

131

00

174

00

v3172

00

123

00

131

00

174

00

v4172

00

123

00

131

00

174

00

v5172

00

123

00

131

00

174

00

v6172

00

123

00

131

00

174

00

Total

2066

02

1479

03

1572

00

2088

00

(e)

Leads

s004

2lrem

s0043lrem

s004

4lrem

s0045lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i152

00

101

00

151

00

164

00

ii151

00

101

00

151

00

164

00

iii151

00

101

00

151

00

164

00

avr

152

00

101

00

151

00

164

00

avl

152

00

101

00

151

00

164

00

avf

151

00

101

00

151

00

164

00

v1151

00

101

00

151

00

164

00

v2151

00

101

00

151

00

164

00

v3151

00

101

00

151

00

164

00

v4151

00

101

00

151

00

164

00

v5151

00

101

00

151

00

164

00

v6151

00

101

00

151

00

164

00

Total

1815

00

1212

00

1812

00

1968

00

(f)

Leads

s004

6lrem

s0047lrem

s0049lrem

s0050lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i162

00

221

00

170

00

121

10

ii162

00

221

00

170

00

121

10

iii162

00

221

00

170

00

122

00

avr

162

00

221

00

170

00

122

00

avl

162

00

221

00

170

00

122

00

avf

162

00

221

00

170

00

122

00

v1162

00

221

00

170

00

122

00

v2162

00

221

00

170

00

122

00

v3162

00

221

00

170

00

122

00

v4162

00

220

10

170

00

122

00

v5162

00

221

00

170

00

122

00

v6162

00

221

00

170

00

121

10

Total

1944

00

2651

10

2040

00

1461

30

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 8: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

8 Computational and Mathematical Methods in MedicineTa

ble2Th

eQRS

detectionof

the12channelsof

theP

TBdatabase

(a)

Leads

s0010rem

s0014lrem

s0015lrem

s0017lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i52

00

166

00

151

00

139

00

ii52

00

165

00

151

00

142

03

iii52

00

165

00

152

01

139

00

avr

520

0166

00

152

00

139

00

avl

520

0166

00

152

01

139

00

avf

520

0165

00

151

00

140

01

v152

00

165

00

151

00

140

01

v252

00

166

00

151

00

139

00

v352

00

166

00

151

00

139

00

v452

00

166

00

151

00

139

00

v552

00

166

00

151

00

139

00

v652

00

165

00

151

00

139

00

Total

624

00

1987

00

1815

02

1673

05

(b)

Leads

s0020arem

s0020brem

s0021arem

s0021brem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

156

00

184

00

183

00

ii159

00

156

00

184

00

183

00

iii159

00

156

00

184

00

183

00

avr

159

00

156

00

184

00

183

00

avl

159

00

156

00

184

00

183

00

avf

158

321

151

520

184

00

183

00

v1159

00

156

00

184

00

183

00

v2159

00

156

00

184

00

183

00

v3159

00

156

00

183

10

183

00

v4158

10

156

00

184

00

183

00

v5159

00

156

00

184

00

183

00

v6159

00

156

00

184

00

183

00

Total

1906

421

1867

520

2207

10

2196

00

(c)

Leads

s0025lrem

s0029lrem

s0031lrem

s0035rem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i199

00

136

00

176

00

460

0ii

199

00

137

00

176

00

460

0iii

197

20

137

00

176

00

460

0avr

197

20

137

00

176

00

460

0avl

197

20

136

00

175

10

460

0avf

199

00

137

00

176

00

460

0v1

199

00

136

00

176

00

460

0v2

199

00

136

00

176

00

460

0v3

199

00

137

00

176

00

460

0v4

199

00

136

00

176

00

460

0v5

199

00

137

00

176

00

460

0v6

199

00

136

00

176

00

460

0Total

2382

60

1638

00

2111

10

552

00

Computational and Mathematical Methods in Medicine 9(d)

Leads

s0036lrem

s0037lrem

s0038lrem

s0039lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i173

01

123

00

131

00

174

00

ii172

00

123

00

131

00

174

00

iii172

00

124

01

131

00

174

00

avr

173

01

123

00

131

00

174

00

avl

172

00

124

01

131

00

174

00

avf

172

00

124

01

131

00

174

00

v1172

00

123

00

131

00

174

00

v2172

00

123

00

131

00

174

00

v3172

00

123

00

131

00

174

00

v4172

00

123

00

131

00

174

00

v5172

00

123

00

131

00

174

00

v6172

00

123

00

131

00

174

00

Total

2066

02

1479

03

1572

00

2088

00

(e)

Leads

s004

2lrem

s0043lrem

s004

4lrem

s0045lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i152

00

101

00

151

00

164

00

ii151

00

101

00

151

00

164

00

iii151

00

101

00

151

00

164

00

avr

152

00

101

00

151

00

164

00

avl

152

00

101

00

151

00

164

00

avf

151

00

101

00

151

00

164

00

v1151

00

101

00

151

00

164

00

v2151

00

101

00

151

00

164

00

v3151

00

101

00

151

00

164

00

v4151

00

101

00

151

00

164

00

v5151

00

101

00

151

00

164

00

v6151

00

101

00

151

00

164

00

Total

1815

00

1212

00

1812

00

1968

00

(f)

Leads

s004

6lrem

s0047lrem

s0049lrem

s0050lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i162

00

221

00

170

00

121

10

ii162

00

221

00

170

00

121

10

iii162

00

221

00

170

00

122

00

avr

162

00

221

00

170

00

122

00

avl

162

00

221

00

170

00

122

00

avf

162

00

221

00

170

00

122

00

v1162

00

221

00

170

00

122

00

v2162

00

221

00

170

00

122

00

v3162

00

221

00

170

00

122

00

v4162

00

220

10

170

00

122

00

v5162

00

221

00

170

00

122

00

v6162

00

221

00

170

00

121

10

Total

1944

00

2651

10

2040

00

1461

30

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

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Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 9: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

Computational and Mathematical Methods in Medicine 9(d)

Leads

s0036lrem

s0037lrem

s0038lrem

s0039lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i173

01

123

00

131

00

174

00

ii172

00

123

00

131

00

174

00

iii172

00

124

01

131

00

174

00

avr

173

01

123

00

131

00

174

00

avl

172

00

124

01

131

00

174

00

avf

172

00

124

01

131

00

174

00

v1172

00

123

00

131

00

174

00

v2172

00

123

00

131

00

174

00

v3172

00

123

00

131

00

174

00

v4172

00

123

00

131

00

174

00

v5172

00

123

00

131

00

174

00

v6172

00

123

00

131

00

174

00

Total

2066

02

1479

03

1572

00

2088

00

(e)

Leads

s004

2lrem

s0043lrem

s004

4lrem

s0045lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i152

00

101

00

151

00

164

00

ii151

00

101

00

151

00

164

00

iii151

00

101

00

151

00

164

00

avr

152

00

101

00

151

00

164

00

avl

152

00

101

00

151

00

164

00

avf

151

00

101

00

151

00

164

00

v1151

00

101

00

151

00

164

00

v2151

00

101

00

151

00

164

00

v3151

00

101

00

151

00

164

00

v4151

00

101

00

151

00

164

00

v5151

00

101

00

151

00

164

00

v6151

00

101

00

151

00

164

00

Total

1815

00

1212

00

1812

00

1968

00

(f)

Leads

s004

6lrem

s0047lrem

s0049lrem

s0050lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i162

00

221

00

170

00

121

10

ii162

00

221

00

170

00

121

10

iii162

00

221

00

170

00

122

00

avr

162

00

221

00

170

00

122

00

avl

162

00

221

00

170

00

122

00

avf

162

00

221

00

170

00

122

00

v1162

00

221

00

170

00

122

00

v2162

00

221

00

170

00

122

00

v3162

00

221

00

170

00

122

00

v4162

00

220

10

170

00

122

00

v5162

00

221

00

170

00

122

00

v6162

00

221

00

170

00

121

10

Total

1944

00

2651

10

2040

00

1461

30

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 10: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

10 Computational and Mathematical Methods in Medicine(g)

Leads

s0051lrem

s0052lrem

s0053lrem

s0054lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i159

00

113

00

179

00

166

20

ii159

00

113

00

179

00

154

102

iii160

00

1130

0179

00

165

20

avr

159

00

113

00

179

00

167

10

avl

159

00

113

00

179

00

167

10

avf

161

02

113

00

179

00

164

40

v1159

00

113

00

179

00

162

50

v2159

00

113

00

179

00

164

40

v3160

00

113

00

179

00

166

20

v4159

00

113

00

179

00

168

00

v5159

00

113

00

179

00

168

00

v6159

00

113

00

179

00

168

00

Total

1912

02

1356

00

2148

00

1979

312

(h)

Leads

s0055lrem

s0056lrem

s0057lrem

s0058lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i115

00

144

00

158

00

168

00

ii116

01

144

00

158

00

168

00

iii115

00

145

00

158

00

168

00

avr

115

00

145

00

158

00

168

00

avl

115

00

144

00

158

00

168

00

avf

115

00

145

00

158

00

168

00

v1115

00

144

00

158

00

168

00

v2115

00

144

00

158

00

168

00

v3115

00

145

00

158

00

169

01

v4115

00

144

00

158

00

168

00

v5115

00

144

00

158

00

168

00

v6115

00

144

00

158

00

168

00

Total

1381

01

1732

00

1896

00

2017

01

(i)

Leads

s0059lrem

s006

0lrem

s0090lrem

s0062lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i150

00

140

00

113

00

124

00

ii150

00

140

00

113

00

124

00

iii150

00

140

00

113

00

124

00

avr

150

00

140

00

113

00

124

00

avl

150

00

140

00

113

00

124

00

avf

150

00

140

00

113

00

124

00

v1150

00

140

00

113

00

124

00

v2150

00

140

00

113

00

124

00

v3150

00

140

00

1140

1124

00

v4150

00

140

00

1140

1124

00

v5150

00

140

00

113

00

124

00

v6150

00

140

00

113

00

124

00

Total

1800

00

1680

00

1358

02

1488

00

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

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PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

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Computational and Mathematical Methods in Medicine

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Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 11: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

Computational and Mathematical Methods in Medicine 11(j)

Leads

s0063lrem

s006

4lrem

s0065lrem

s006

6lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

149

10

142

00

126

00

ii151

30

150

00

142

00

127

01

iii154

00

150

00

142

00

126

00

avr

154

00

150

00

142

00

126

00

avl

154

00

149

10

142

00

126

00

avf

154

00

150

00

142

00

126

00

v1154

00

150

00

142

00

126

00

v2154

00

150

00

142

00

126

00

v3154

00

150

00

142

00

126

00

v4154

00

150

00

142

00

126

00

v5154

00

150

00

142

00

126

00

v6154

00

149

10

142

00

126

00

Total

1845

30

1797

30

1704

00

1513

01

(k)

Leads

s0067lrem

s006

8lrem

s0069lrem

s0070lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i35

00

115

00

990

0165

00

ii35

00

115

00

990

0165

00

iii36

01

113

211

990

0165

00

avr

350

0115

00

990

0165

00

avl

360

1116

02

990

0165

00

avf

360

1115

00

990

0165

00

v135

00

1160

299

00

165

00

v235

00

1141

099

00

167

01

v336

01

115

00

990

0166

00

v435

00

1141

099

00

165

00

v535

00

1141

099

00

165

00

v635

00

115

00

990

0165

00

Total

424

04

1377

515

1188

00

1983

01

(l)

Leads

s0071lrem

s0072lrem

s0073lrem

s0074lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i154

00

170

00

177

00

950

0ii

154

00

170

00

177

00

950

0iii

154

00

170

00

173

50

950

0avr

154

00

170

00

178

00

950

0avl

154

00

170

00

177

00

950

0avf

154

00

170

00

177

00

950

0v1

154

00

170

00

178

00

950

0v2

154

00

170

00

178

00

950

0v3

154

00

170

00

178

00

950

0v4

154

00

170

00

177

00

950

0v5

154

00

170

00

177

00

950

0v6

154

00

170

00

178

00

950

0Total

1848

00

2040

00

2125

50

1140

00

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 12: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

12 Computational and Mathematical Methods in Medicine(m

)

Leads

s0075lrem

s0076lrem

s0077lrem

s0078lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i121

00

109

00

141

00

102

00

ii121

00

109

00

141

00

103

01

iii121

00

109

00

141

00

102

00

avr

121

00

109

00

141

00

102

00

avl

121

00

109

00

141

00

102

00

avf

121

00

109

00

141

00

102

00

v1122

01

109

00

141

00

102

00

v2121

00

109

00

141

00

102

00

v3121

00

109

00

141

00

102

00

v4121

00

109

00

141

00

102

00

v5121

00

109

00

141

00

102

00

v6121

00

109

00

141

00

102

00

Total

1453

01

1308

00

1692

00

1225

01

(n)

Leads

s0079lrem

s0080lrem

s0093lrem

s0082lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i135

00

130

00

104

00

133

00

ii135

00

129

00

104

00

133

00

iii135

00

130

00

104

00

134

00

avr

135

00

130

00

104

00

134

00

avl

135

00

130

00

104

00

133

00

avf

135

00

129

00

105

01

133

00

v1135

00

129

00

104

00

134

00

v2135

00

129

00

104

00

133

00

v3135

00

130

00

104

00

134

00

v4135

00

130

00

104

00

134

00

v5135

00

130

00

104

00

134

00

v6135

00

130

00

104

00

133

00

Total

1620

00

1556

00

1249

01

1602

00

(o)

Leads

s0083lrem

s0084lrem

s0085lrem

s0097lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i122

00

122

00

106

00

178

01

ii123

10

122

00

106

00

177

00

iii122

00

122

00

108

02

178

00

avr

122

00

122

00

106

00

178

00

avl

122

00

122

00

106

00

178

00

avf

122

00

122

00

106

00

178

00

v1122

00

122

00

106

00

177

00

v2122

00

122

00

108

02

178

00

v3122

00

122

00

106

00

178

00

v4122

00

122

00

106

00

178

00

v5122

00

122

00

106

00

178

00

v6122

00

122

00

106

00

177

00

Total

1465

10

1464

00

1276

04

2133

01

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 13: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

Computational and Mathematical Methods in Medicine 13(p)

Leads

s0101lrem

s0103lrem

s0149lrem

s0152lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i125

00

106

01

131

00

128

00

ii125

00

106

00

131

00

128

00

iii125

00

107

01

131

00

128

00

avr

125

00

106

00

131

00

128

00

avl

125

00

106

00

131

00

128

00

avf

125

00

106

00

131

00

124

40

v1125

00

106

00

131

00

128

00

v2125

00

106

00

131

00

128

00

v3125

00

106

00

131

00

128

00

v4125

00

106

00

131

00

128

00

v5125

00

106

00

131

00

128

00

v6125

00

106

00

131

00

128

00

Total

1500

00

1273

02

1572

00

1532

40

(q)

Leads

s0087lrem

s0088lrem

s0089lrem

s0091lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i142

00

144

00

196

00

1141

0ii

138

40

144

00

194

20

115

00

iii136

60

144

00

196

00

115

00

avr

138

00

144

00

196

00

115

00

avl

138

00

144

00

196

00

115

00

avf

137

10

144

00

196

00

1160

1v1

136

00

144

00

196

00

115

00

v2138

00

144

00

196

00

115

00

v3137

10

144

00

196

00

115

00

v4138

00

144

00

196

00

115

00

v5138

00

144

00

156

400

115

00

v6138

00

144

00

196

00

115

00

Total

1654

120

1728

00

2310

420

1380

11

(r)

Leads

s0095lrem

s0096lrem

s0150lrem

s0150lrem

TPFN

FPTP

FNFP

TPFN

FPTP

FNFP

i149

10

217

00

00

0132

00

ii150

00

217

00

00

0132

00

iii150

00

217

00

00

0132

00

avr

150

00

217

00

00

0132

00

avl

150

00

217

00

00

0132

00

avf

150

00

217

00

00

0132

00

v1149

10

217

00

00

0132

00

v2149

10

217

00

00

0131

10

v3150

00

217

00

00

0132

00

v4150

00

217

00

00

0132

00

v5150

00

216

10

00

0132

00

v6150

00

217

00

00

0132

00

Total

1797

30

2603

10

00

01583

10

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 14: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

14 Computational and Mathematical Methods in Medicine

(s)

Leads

s0291lrem

s0292lrem

TPFN

FPTP

FNFP

i129

00

132

00

ii129

00

132

00

iii129

00

132

00

avr

129

00

132

00

avl

129

00

132

00

avf

129

00

132

00

v1129

00

132

00

v2129

00

132

00

v3129

00

132

00

v4129

00

132

00

v5129

00

132

00

v6129

00

132

00

Total

1548

00

1584

00

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 15: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

Computational and Mathematical Methods in Medicine 15

achieved showed that sensitivity (Se) equals 99924 detec-tion error rate (DER) equals 0155 Positive Predictivity (+P)equals 99922 and Classification Accuracy was 99846

4 Conclusion

In the present study the most common methods to removenoise in the ECG signal are evaluated A Shannon energy-based approach to determine the QRS complex of the12-lead ECG signal is provided ECG signal is selected witha variety of physiology from the PTBDatabase and examinedby Octave software Accuracy and sensitivity achieved fromTable 1 showed that the presented algorithm is fast and simplewithout complex equations This algorithm does not need ahigh memory and high hardware Diagnosis time for eachlead is approximately 25 seconds based onOctaveThe resultsshowed that algorithm detection has very little lag less than0013 seconds without error This lag is generated from stage3

Appendix

See Table 2

Competing Interests

The authors declare that they have no competing interests

References

[1] K Wang S Ma J Feng W Zhang M Fan and D ZhaoldquoDesign of ECG signal acquisition system based on DSPrdquo inProceedings of the International Workshop on Information andElectronics Engineering (IWIEE rsquo12) vol 29 pp 3763ndash3767Harbin China March 2012

[2] Y-C Yeh and W-J Wang ldquoQRS complexes detection for ECGsignal the difference operation methodrdquo Computer Methodsand Programs in Biomedicine vol 91 no 3 pp 245ndash254 2008

[3] M Yochum C Renaud and S Jacquir ldquoAutomatic detection ofP QRS and T patterns in 12 leads ECG signal based on CWTrdquoBiomedical Signal Processing and Control vol 25 pp 46ndash532016

[4] F Jin L Sugavaneswaran S Krishnan and V S ChauhanldquoQuantification of fragmented QRS complex using intrinsictime-scale decompositionrdquo Biomedical Signal Processing andControl vol 31 pp 513ndash523 2017

[5] H Zhang ldquoAn improved QRS wave group detection algorithmandmatlab implementationrdquo Physics Procedia vol 25 pp 1010ndash1016 2012

[6] D Sadhukhan and M Mitra ldquoR-peak detection algorithmfor Ecg using double difference and RR interval processingrdquoProcedia Technology vol 4 pp 873ndash877 2012

[7] J Pan and W J Tompkins ldquoA real-time QRS detection algo-rithmrdquo IEEE Transactions on Biomedical Engineering vol 32no 3 pp 230ndash236 1985

[8] Y Zou J Han S Xuan et al ldquoAn energy-efficient design forECG recording and R-peak detection based on wavelet trans-formrdquo IEEE Transactions on Circuits and Systems II ExpressBriefs vol 62 no 2 pp 119ndash123 2015

[9] J Yan and L Lu ldquoImproved Hilbert-Huang transform basedweak signal detectionmethodology and its application on incip-ient fault diagnosis and ECG signal analysisrdquo Signal Processingvol 98 pp 74ndash87 2014

[10] V J Ribas Ripoll A Wojdel E Romero P Ramos and JBrugada ldquoECG assessment based on neural networks withpretrainingrdquoApplied SoftComputing vol 49 pp 399ndash406 2016

[11] J Mateo A M Torres M A Garcıa and J L SantosldquoNoise removal in electroencephalogram signals using an arti-ficial neural network based on the simultaneous perturbationmethodrdquo Neural Computing and Applications vol 27 no 7 pp1941ndash1957 2016

[12] B-U Kohler C Hennig and R Orglmeister ldquoThe principlesof software QRS detectionrdquo IEEE Engineering in Medicine andBiology Magazine vol 21 no 1 pp 42ndash57 2002

[13] J Bo X Cao Y Wan et al ldquoInvestigation performance onelectrocardiogram signal processing based on an advancedalgorithm combining wavelet packet transform (WPT) andHilbert-Huang Transform (HHT)rdquo Lecture Notes in ElectricalEngineering vol 269 pp 959ndash968 2014

[14] M Lagerholm and G Peterson ldquoClustering ECG complexesusing hermite functions and self-organizingmapsrdquo IEEE Trans-actions on Biomedical Engineering vol 47 no 7 pp 838ndash8482000

[15] M Akhbari M B Shamsollahi O Sayadi A A Armoundasand C Jutten ldquoECG segmentation and fiducial point extractionusing multi hidden Markov modelrdquo Computers in Biology andMedicine vol 79 pp 21ndash29 2016

[16] M Brajovic I Orovic M Dakovic and S Stankovic ldquoOn theparameterization of Hermite transform with application to thecompression of QRS complexesrdquo Signal Processing vol 131 pp113ndash119 2017

[17] R Jane A Blasi J Garcia and P Laguna ldquoEvaluation of anautomatic threshold based detector ofwaveform limits inHolterECG with the QT databaserdquo in Computers in Cardiology 1997pp 295ndash298 IEEE Computer Society Press Piscataway NJUSA 1997

[18] M SManikandan andK P Soman ldquoA novelmethod for detect-ing R-peaks in electrocardiogram (ECG) signalrdquo BiomedicalSignal Processing and Control vol 7 no 2 pp 118ndash128 2012

[19] Z Zidelmal A AmirouDOuld-AbdeslamAMoukadem andA Dieterlen ldquoQRS detection using S-Transform and Shannonenergyrdquo Computer Methods and Programs in Biomedicine vol116 no 1 pp 1ndash9 2014

[20] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysioToolkit and PhysioNet components of a new researchresource for complex physiologic signalsrdquo Circulation vol 101no 23 pp E215ndashE220 2000

[21] PhysioNet The PTB Diagnostic ECG Database (ptbdb) httpphysionetorgphysiobankdatabaseptbdb

[22] A R Verma and Y Singh ldquoAdaptive tunable notch filter forECG signal enhancementrdquo Procedia Computer Science vol 57pp 332ndash337 3rd International Conference on Recent Trends inComputing 2015 (ICRTC-2015) 2015

[23] X Ning IW Selesnick and L Duval ldquoChromatogram baselineestimation and denoising using sparsity (BEADS)rdquo Chemomet-rics and Intelligent Laboratory Systems vol 139 pp 156ndash1672014

[24] H Zhu and J Dong ldquoAn R-peak detection method based onpeaks of Shannon energy enveloperdquo Biomedical Signal Process-ing and Control vol 8 no 5 pp 466ndash474 2013

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 16: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

16 Computational and Mathematical Methods in Medicine

[25] D Schlichtharle ldquoAnalog filtersrdquo in Digital Filters Basics andDesign pp 19ndash83 Springer Berlin Germany 2011

[26] B T Ricardo Ferro A Ramırez Aguilera and R R FernandezDe La Vara Prieto ldquoAutomated detection of the onset andsystolic peak in the pulse wave using Hilbert transformrdquoBiomedical Signal Processing and Control vol 20 article no 672pp 78ndash84 2015

[27] V K Ingle and J G Proakis Digital Signal Processing UsingMATLAB BrooksCole Boston Mass USA 2000

[28] M Rakshit D Panigrahy and P K Sahu ldquoAn improvedmethodfor R-peak detection by using Shannon energy enveloperdquoSadhana Academy Proceedings in Engineering Sciences vol 41no 5 pp 469ndash477 2016

[29] GNU-Octave httpswwwgnuorgsoftwareoctave[30] M Merah T A Abdelmalik and B H Larbi ldquoR-peaks detec-

tion based on stationary wavelet transformrdquoComputerMethodsand Programs in Biomedicine vol 121 no 3 pp 149ndash160 2015

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 17: ResearchArticle Shannon’s Energy Based Algorithm in ECG ...downloads.hindawi.com/journals/cmmm/2017/8081361.pdf · ResearchArticle Shannon’s Energy Based Algorithm in ECG Signal

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom


Capacity crunch solution - networks.cs.ucdavis.edunetworks.cs.ucdavis.edu/presentation2020/Tanjila_Jan_10_2020.pdf · Shannon’s Capacity Equation Shannon’s capacity equation,

I-Measure. Recall Shannon’s Information measures

RESEARCHARTICLE OpenAccess ......AdrionandMansmannBMCMedicalResearchMethodology2012,12:137 RESEARCHARTICLE OpenAccess Bayesianmodelselectiontechniquesas

RESEARCHARTICLE OutlierAnalysisDefinesZincFingerGene ... · RESEARCHARTICLE OutlierAnalysisDefinesZincFingerGene FamilyDNAMethylationinTumorsand SalivaofHeadandNeckCancerPatients

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SHANNON’S STORY

RESEARCHARTICLE VitaminCCompoundMixturesPrevent Ozone ... · RESEARCHARTICLE VitaminCCompoundMixturesPrevent Ozone-InducedOxidativeDamageinHuman KeratinocytesasInitialAssessmentof

Classification accuracy analyses using Shannon’s Entropy

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RESEARCHARTICLE AlteredBrainActivationduringEmotional ... · RESEARCHARTICLE AlteredBrainActivationduringEmotional FaceProcessinginRelationtoBothDiagnosis andPolygenicRiskofBipolarDisorder

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Welcome to Mrs. Crocker & Mrs. Shannon’s Kindergarten Class Website-

Beyond the Shannon’s Bound

Shannon’s entropy and Its Generalizations towards

RESEARCHARTICLE PotassiumRetentionunderSaltStressIs ......RESEARCHARTICLE PotassiumRetentionunderSaltStressIs AssociatedwithNaturalVariationinSalinity ToleranceamongArabidopsisAccessions

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Outline Building Blocks Shannon’s Theorem Encoding

RESEARCHARTICLE DecellularizationandDelipidationProtocols ... · RESEARCHARTICLE DecellularizationandDelipidationProtocols ofBovineBoneandPericardiumforBone GraftingandGuidedBoneRegeneration

RESEARCHARTICLE AnalyticApproximateSolutionstothe ...€¦ · RESEARCHARTICLE AnalyticApproximateSolutionstothe BoundaryLayerFlowEquationovera StretchingWallwithPartialSlipatthe Boundary

RESEARCHARTICLE 3DComputationalMechanicsElucidatethe ... · RESEARCHARTICLE 3DComputationalMechanicsElucidatethe EvolutionaryImplicationsofOrbitPosition andSizeDiversity ofEarlyAmphibians

RESEARCHARTICLE WhichSpeciesAreWeResearchingandWhy ...€¦ · RESEARCHARTICLE WhichSpeciesAreWeResearchingandWhy? ACaseStudyoftheEcologyofBritish BreedingBirds AilsaJ.McKenzie1*,Peter

Origin of Life on Earth and Shannon’s

RESEARCHARTICLE Age-RelatedChangesinOcularBlood ... · RESEARCHARTICLE Age-RelatedChangesinOcularBlood VelocityinSuspectswithGlaucomatousOptic DiscAppearance.ComparisonwithHealthy

Shannon’s School Based Telemedicine Program

Shannon’s capacity analysis of GEPOF for technical

RESEARCHARTICLE LateralSemicircularCanalAsymmetryin ... · RESEARCHARTICLE LateralSemicircularCanalAsymmetryin IdiopathicScoliosis:AnEarlyLinkbetween Biomechanical,HormonalandNeurosensory

Shannon’s entropy usage as statistic in assessment of

RESEARCHARTICLE IdentificationofMultipleProteinsCoupling ... · RESEARCHARTICLE IdentificationofMultipleProteinsCoupling TranscriptionalGeneSilencingtoGenome StabilityinArabidopsisthaliana

RESEARCHARTICLE MagnetohydrodynamicFlowbyaStretching ... · RESEARCHARTICLE MagnetohydrodynamicFlowbyaStretching CylinderwithNewtonianHeatingand Homogeneous-HeterogeneousReactions

RESEARCHARTICLE EEGThetaDynamicswithinFrontaland ... · RESEARCHARTICLE EEGThetaDynamicswithinFrontaland ParietalCorticesforErrorProcessingduring ReachingMovementsinaPrismAdaptation

RESEARCHARTICLE TransientChangesinBacterioplankton ... · RESEARCHARTICLE TransientChangesinBacterioplankton CommunitiesInducedbytheSubmarine VolcanicEruptionofElHierro(Canary Islands)

RESEARCHARTICLE ElectricalSignalsinPrayerPlants ...€¦ · RESEARCHARTICLE ElectricalSignalsinPrayerPlants (Marantaceae)?InsightsintotheTrigger MechanismoftheExplosiveStyleMovement

RESEARCHARTICLE SelectionandSpreadofArtemisinin … · RESEARCHARTICLE SelectionandSpreadofArtemisinin-Resistant AllelesinThailandPriortotheGlobal ArtemisininResistanceContainment

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RESEARCHARTICLE ElisidepsinInteractsDirectlywith ... · RESEARCHARTICLE ElisidepsinInteractsDirectlywith GlycosylceramidesinthePlasmaMembrane ofTumorCellstoInduceNecroticCellDeath