neuromyopathy disease detection using wavelet packet based denoising technique

International Journal of Medical and Biological Frontiers ISSN: 1081-3829

Volume 20, Number 1 © Nova Science Publishers, Inc.

NEUROMYOPATHY DISEASE DETECTION USING

WAVELET PACKET BASED DENOISING TECHNIQUE

Akash Kumar Bhoi1

, Karma Sonam Sherpa1

and Devakishore Phurailatpam2

1Department of AE & I Engg, Sikkim Manipal Institute of Technology (SMIT), Majitar

2Department of E&E Engg, National Institute of Technology, Manipur

ABSTRACT

The wavelet packet based filtering/denoising performance is analyzed by using

Balance Sparsity-norm & fixed form thresholding (soft &hard) methods where the Mean,

Standard Deviation (SD) & Mean Absolute Deviation (MAD) is calculated at different

global threshold for healthy, myopathic & neuropathic EMG signals. The intension is to

extract the residuals of healthy and diseased EMG signals which provide the significant

results for classification of healthy, myopathic & neuropathic EMG signals. The features

are extracted or the coefficients are generated using “haar-3”. These two methods have a

fairly large accuracy percentage which can be used as a diagnostic tool in medical field.

The technique mentioned in this paper is a mathematical tool for the detection of

myopathy and neuropathy as compared to the conventional instrumental ones. Hence, it is

faster, efficient and robust as it is resistant to environmental hazards.

Keywords: EMG, myopathy, neuropathy, wavelet packet, balance sparsity-norm, fixed form

thresholding

1. INTRODUCTION

In the diagnosis of neuromuscular disorders, the needle Electromyogram (EMG)

techniques are dominant, but attempts to use surface EMG (sEMG) for the diagnostic

purposes are appearing as well [4], [8], [10]. Because of the vast differences between sEMG

and conventional electrodiagnostic techniques, the American Association of Electrodiagnostic

Medicine published a review of clinical utility of sEMG [3].

They compared conventional techniques that are used in needle EMG examination and

tried to find their sEMG equivalents. Procedures that can be conducted using both EMG

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Akash Kumar Bhoi, Karma Sonam Sherpa and Devakishore Phurailatpam 14

techniques include: single MUAP analysis (amplitude, duration and configuration) and

studies of muscle fibre conduction velocity.

The procedures that cannot be done using sEMG include: measuring an insertional

activity of the needle, fibrillation potentials and precise localization of the lesion. Regardless

to the type of EMG used, the relationship between various kinds of pathological changes of

the motor units and the shape of the EMG signal is difficult to establish [11]. This is affecting

computer-based diagnosis the most, because in order to obtain good classification results each

group should have distinct features that would enable to distinguish among them easily. In

search of such features, various sEMG parameters have been investigated.

Many studies relied only on one group of patients, i.e. only myopathic or neuropathic, but

in order to make a complete method evaluation, both groups as well as a control group should

be included, like in [4] and [8].

An overview and comparison of different EMG methods for the myopathy evaluation are

presented in [1].

Different EMG methods can be helpful when diagnosing myopathies, whereas the most

useful are: manual analysis of the individual motor unit action potentials (MUAPs) and turns–

amplitude analyses. Analysis of the firing rate of motor units, power spectrum analysis, as

well as multi-channel surface EMG may also be used in the diagnostics, but are not so

common [1]. In the muscles of patients with myopathy, both the degeneration and

regeneration of muscle fibres are reflected by short-duration, low-amplitude and polyphasic

shape of individual MUAPs [1].

Another parameter to be analysed is the motor unit firing rate. With myopathy, early

recruitment may be seen, i.e. many MUAPs are present for the level of muscle contraction

compared to normal subjects, due to the weakness of the muscle. The frequency spectrum of

EMG can also be used, also shown the analysis of individual MUAPs which, was more

sensitive for detecting myopathy than the analysis of the EMG signal frequency spectrum [1].

In [10], the MU size parameter was investigated.

It was hypothesised that the size of a MU, defined as the number of muscle fibres

innervated by a single motor neuron, is an important parameter in differentiating neuropathic

from myopathic properties. Multi-channel sEMG was used to assess the MU size. Single

MUAPs were extracted from sEMG signal with the help of a decomposition technique, and

the properties of individual MUAPs were compared. They found out the MUAP amplitude is

significantly higher in neuropathic patients.

The next parameter studied for sEMG was muscle fibre conduction velocity. It was

shown that myopathic patients can be separated from healthy subjects using mean muscle

fibre conduction velocity and propagation of MUAPs [9]. Signals of myopathic patients

didn‟t show propagation behaviour of MUAPs. Also the centre of the innervation zone

couldn‟t be delimited as distinctly as in the normal case. Another study reported the

disturbance of MUAP propagation at myopathy [13, 15].

A. Neuropathy

Peripheral neuropathy is damage to nerves of the peripheral nervous system, which may

be caused either by diseases of or trauma to the nerve or the side effects of systemic illness.

The four cardinal patterns of peripheral neuropathy are polyneuropathy, mononeuropathy,

Neuromyopathy Disease Detection Using Wavelet Packet … 15

mononeuritis multiplex and autonomic neuropathy. The most common form is (symmetrical)

peripheral polyneuropathy, which mainly affects the feet and legs. The form of neuropathy

may be further broken down by cause, or the size of predominant fiber involvement, i.e., large

fiber or small fiber peripheral neuropathy. The cause of a neuropathy cannot be identified

frequently, and it is designated as being idiopathic.

Neuropathy may be associated with varying combinations of weakness, autonomic

changes, and sensory changes. Loss of muscle bulk or fasciculations, a particular fine

twitching of muscle, may be seen. Sensory symptoms encompass loss of sensation and

"positive" phenomena including pain. Symptoms depend on the type of nerves affected

(motor, sensory, or autonomic) and location of the nerves in the body. One or more types of

nerves may be affected. Common symptoms associated with damage to the motor nerve are

muscle weakness, cramps, and spasms. Loss of balance and coordination may also occur.

Damage to the sensory nerve can produce tingling, numbness, and a burning pain. Pain

associated with this nerve is described in various ways such as the following: burning,

freezing, or electric-like, extreme sensitivity to touch.

The autonomic nerve damage causes problems with involuntary functions leading to

symptoms such as abnormal blood pressure and heart rate, reduced ability to perspire,

constipation, bladder dysfunction (such as incontinence), and sexual dysfunction.

B. Myopathy

In medicine, a myopathy is a muscular disease in which the muscle fibers do not function

for any one of many reasons, resulting in muscular weakness. "Myopathy" simply means

muscle disease This meaning implies that the primary defect is within the muscle, as opposed

to the nerves. Muscle cramps, stiffness, and spasm can also be associated with myopathy.

Muscular disease can be classified as neuromuscular or musculoskeletal in nature. Some

conditions, such as myositis, can be considered both neuromuscular and musculoskeletal.

Myopathies in systemic disease results from several different disease processes including

endocrine, inflammatory, paraneoplastic, infectious, drug- and toxin-induced, critical illness

myopathy, metabolic, and myopathies with other systemic disorders. Patients with systemic

myopathies often present acutely or sub acutely.

On the other hand, familial myopathies or dystrophies generally present in a chronic

fashion (with exceptions of metabolic myopathies) where symptoms, on occasion, can be

precipitated acutely. Most of the inflammatory myopathies can have a chance association

with malignant lesions; the incidence appears to be specifically increased only in patients

with dermatomyositis.

2. WAVELET PACKET (1-D) BASED DE-NOISING

The EMG is collected from PhysioBank ATM having 4000 samples of healthy,

myopathic & neuropathic EMG signals with the recorded length of 10 seconds (Figure 2-4).

The simulation part is carried out in Matlab platform. The input EMG signal is analyzed by

applying wavelet „haar‟ with level-3 & the selected entropy is Shannon. Figure 1 shows the

proposed methodology for extacting the attributes from EMG signals.


Figure 1. Block diagram of the proposed methodology.

Figure 2. Input healthy EMG Signal having length 40000 samples.

Figure 3. Input myopathic EMG Signal having length 40000 samples.

Figure 4. Input Neuropathic EMG Signal having length 40000 samples.


A. Wavelet Packet Decomposition

Originally known as Optimal Subband Tree Structuring (SB-TS) also called Wavelet

Packet Decomposition (WPD) (sometimes known as just Wavelet Packets or Subband Tree)

is a wavelet transform where the discrete-time (sampled) signal is passed through more filters

than the discrete wavelet transform (DWT).In the DWT, each level is calculated by passing

only the previous wavelet approximation coefficients (cAj) through discrete-time low and

high pass quadrature mirror filters. However in the WPD, both the detail (cDj (in the 1-D

case), cHj, cVj, cDj (in the 2-D case) and approximation coefficients are decomposed to create

the full binary tree (Figure 5).

For n levels of decomposition the WPD produces 2n different sets of coefficients

(or nodes) as opposed to (3n + 1) sets for the DWT. However, due to the down sampling

process the overall number of coefficients is still the same and there is no redundancy [15].

Figure 5. Wavelet Packet decomposition over 3 levels. g[n] is the low-pass

approximation coefficients, h[n] is the high-pass detail coefficients.

For fixed from soft threshold denoising process for healthy EMG signal (shown in

Table1) continues upto 8000 (global threshold limit) where the mean is -1.002e-015, the

standard deviation is 382.9and the MAD is 174.4 whereas for the hard thesholding the

threshold unvarying limit is 9000 and the mean is -1.002e-015, the SD is 382.9 and the MAD

is 174.4

For balance sparsity-norm soft threshold denoising process for healthy EMG signal

(shown in Table 2) continues upto 9000 (global threshold limit) where the mean is -1.002e-

015, the SD is 382.9 and the MAD is 174.4 whereas, for the hard thesholding, the threshold

unvarying limit is 8000 and the mean is -1.002e-015, the SD is 382.9 and the MAD is 174.4.


Table 1. Mean, Standard Deviation (SD) & Mean Absolute Deviation (MAD) of Fixed

from thersholding for Healthy EMG signal

Fixed from

thld

Soft Hard

Global thld

limit

Mean SD MAD Mean SD MAD

0 -2.302e-015 3.6e-01 0 -2.302e-015 3.6e-01 0

100 -2.612e-015 58.19 43.5 -1.466e-015 29.34 21.54

1000 -8.782e-o15 254 136.6 -8.129e-016 186.8 109.1

2000 -8.2e-016 329.1 161.4 -5.016e-016 280.2 145.4

3000 -7.063e-016 360 169.7 -5.471e-016 328.3 160.5

4000 -9.109e-016 372.7 172.5 -7.063e-016 358.5 168.9

5000 -1.025e-015 377.8 173.5 -9.337e-016 366.9 171.1

6000 -1.047e-015 380.8 174.1 -1.184e-015 379 173

7000 -1.275e-015 382.2 174.3 -1.184e-015 379 173.7

8000 -1.002e-015 382.9 174.4 -1.002e-015 380.8 174.1

9000 -1.002e-015 382.9 174.4 -1.002e-015 382.9 174.4

10000 -1.002e-015 382.9 174.4 -1.002e-015 382.9 174.4

Table 2. Mean, Standard Deviation (SD) & Mean Absolute Deviation (MAD) of Balance

Sparsity-Norm Thersholding for Healthy EMG signal

Balance

Sparsity-norm

Soft Hard

Global thld

limit


0 -2.302e-015 3.6e-01 0 -2.302e-015 3.6e-01 0

100 -1.466e-015 29.34 21.54 -1.466e-015 29.34 21.54

1000 -8.129e-016 186.8 109.1 -8.129e-016 186.8 109.1

2000 -5.016e-016 280.2 145.4 -5.016e-016 280.2 145.4

3000 -5.471e-016 328.3 160.5 -5.471e-016 328.3 160.5

4000 -7.063e-016 358.5 168.9 -7.063e-016 358.5 168.9

5000 -9.337e-016 366.9 171.1 -9.337e-016 366.9 171.1

6000 -9.564e-016 373.6 172.5 -9.564e-016 373.6 172.5

7000 -1.184e-015 379 173.7 -1.184e-015 379 173.7

8000 -1.002e-015 380.8 174.1 -1.002e-015 382.9 174.4

9000 -1.002e-015 382.9 174.4 -1.002e-015 382.9 174.4

10000 -1.002e-015 382.9 174.4 -1.002e-015 382.9 174.4

For fixed from soft threshold denoising process for myopathic EMG signal (shown in

Table 3) continues upto 8000 (global threshold limit) where the mean is -4.278e-015, the SD

is 704.6 and the MAD is 396.3 where as for the hard thesholding, limit unvary at 8000 and

the mean is -4.278e-015, the SD is 704.6 and the MAD is 396.3. These results are quite

higher as compared to healthy EMG signal.

Mean, SD & MAD of balance sparsity-norm for myopathic EMG signal for both soft &

hard thresholding are same with that of Fixed from soft thresholding.

The residual values of neuropathic EMG signal are quite high and dissimilar with

myopathic & healthy EMG signals. Table 4 shows the mathematical attributes of neuropathic


signal which helps in further classification of EMG signals. The fixed from thersholding &

balance sparsity-norm threshoding techniques performed in similar manner.


from Thersholding for Myopathic EMG signal

Fixed

from thld

Soft Hard

Global

thld limit


0 -4.5717e-015 4.293e-013 0 -4.5717e-015 4.293e-013 0

100 -4.3e-015 72.06 54.79 -3.792e-015 26.77 18.65

1000 -4.25e-015 416.7 266.4 -4.233e-015 273 180.7

2000 -4.006e-015 516.9 344.6 -4.801e-015 460.6 288.6

3000 -4006e-015 649.6 375.6 -4.71e-015 573.6 341.6

4000 -4.142e-015 684.2 388.9 -4.642e-015 636.7 369.3

5000 -3.96e-015 698.1 394.1 -4.46e-015 676.7 385.2

6000 -4.233e-015 703.2 395.9 -4.688e-015 697.4 393.6

7000 -4.369e-015 704.4 396.3 -4.597e-015 701.8 395.3

8000 -4.278e-015 704.6 396.3 -4.278e-015 704.6 396.3

9000 -4.278e-015 704.6 396.3 -4.278e-015 704.6 396.3

10000 -4.278e-015 704.6 396.3 -4.278e-015 704.6 396.3


from Thersholding for Neuropathic EMG signal

Fixed

from thld

Soft Hard

Global

thld limit


0 -1.837e-015 1.037e-012 0 -1.837e-015 1.037e-012 0

100 -2.062e-015 61.58 46.49 -2.167e-015 28.58 20.99

1000 -4.603e-015 332.2 175.3 -2.466e-015 167.3 103.5

5,000 -6.399e-015 968.9 373.8 -6.013e-015 642.7 275.9

10,000 -3.648e-015 1398 470.1 -4.103e-015 1000 378.6

15,000 -3.148e-015 1667 518.4 -4.148e-015 1354 456.7

20,000 -3.33e-015 1798 539.1 -4.878e-015 1629 507.9

25,000 -5.149e-015 1860 550.2 -5.876e-015 1760 531.6

30,000 -6.24e-015 1881 554.1 -6.24e-015 1875 553

35,000 -6.604e-015 1882 554.2 -6.604e-015 1882 554.2

40,000 -6.604e-015 1882 554.2 -6.604e-015 1882 554.2

50,000 -6.604e-015 1882 554.2 -6.604e-015 1882 554.2

CONCLUSION

Neuromuscular diseases affect the structure of nerve and muscle cells. Many are

degenerative and certain conditions can cause complete immobilisation as well as being


potentially fatal. Different conditions affect different muscle groups. Early diagnosis is

important for two reasons. Firstly, it allows the patient to build up their muscles before the

onset of degeneration, thus extending the amount of time that they have mobility. Secondly,

the careful prescription of drugs can slow the degeneration. It is therefore important to devise

accurate methods of diagnosis. Two threshoding methods for denoising one-dimentional

signals using wavelet packets are described in this paper. The algorithm performs quite well

in term of both numerical and visual distortion. The tabulation shows the signals residuals

values of the filtering performance at different level of thresholding which clearly classify

between healthy and neuro-myopathic EMG signals with its significant attributes. We have

also analysed filtering performance by plotting the original and de-noised signal in the same

plot. The future research involves with the compression of neuromyopathic signals and the

characteristic change during compression process using wavelet packet.

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