Application of The Wavelet Analysis In Detection of Fault Diagnosis of Heart

Submitted By

D. T. INGOLE (Prof. Ram Meghe Institute of Technology &

Research,Badnera,Amravati)

MIR-DAY

Objective:• Traditional System limitations :

– Conventional approach requires human experts– Human experts need to learn– Accuracy is major criteria– False detection cause erroneous interpretation

So to overcome these limitations • System proposes :• An integrated system for ECG diagnosis that combines WT for feature extraction &

ANN for classification• Using WT, the characteristic points can be localized objectively & accurately.• ANN is used for classification of diseases.• Both supervised & unsupervised NN results in accuracy @ 98%

– Fast detection of ECG parameters using Wavelet– Improved Accuracy in diagnosis using neural network

ELECTROCARDIOGRAM (ECG):What is ECG?

It is a graphic produced by an electrocardiograph, which records the electrical activity of the heart.

Why is ECG done?It is used to detect and locate the source of heart problem. An ECG gives two kinds of information:First, time intervals of the ECG.Second, the amount of electrical activity passing through the heart.

Some of the signs and symptoms that might be evaluated with an ECG include:• Chest pain • Difficulty breathing• Heart pounding, racing, or fluttering, or the sense that your heart is beating

unevenly • Feeling tired and weak.

How Heart Works?

• Heart Cross-Section :

ECG Signal :

• Generation of ECG:

• Following are the steps:Step1. The P wave is a record of the electrical activity through the upper heart chambers (atria).

Sinoatrial Nodal Impulse Origin Atrial Depolarisation

Atrioventricular Nodal Depolarisation

Step2. The QRS complex is a record of the movement of electrical

impulses through the lower heart chambers (ventricles).

Septal Depolarisation Early Ventricular Free Wall Depolarisation

Late Ventricular Free Wall Depolarisation

Step 3. The ST segment corresponds to the time when the ventricle is

contracting but no electricity is flowing through it.

Ventricular SystoleStep 4. The T wave corresponds to the period when the lower heart chambers are relaxing electrically and preparing for their next muscle contraction.

Ventricular Free Wall Polarisation

ECG leads:

• An electrocardiogram is obtained by measuring electrical potential between various points of the body using a biomedical instrumentation amplifier.

• A lead records the electrical signals of the heart from a particular combination of

recording electrodes which are placed at specific points on the patient's body .• Limb leads : Leads I, II and III are the so-called limb leads.

• Proper placement of limb lead

• Precordial leads :The precordial leads V1, V2, V3, V4, V5, and V6 are placed directly on the chest.

Leads V1, V2, and V3 are referred to as the right precordial leads and V4, V5, and V6 are referred to as the left precordial leads.

Waves and Intervals of the ECG:

• Proposed Work : 1. To extract features using wavelet –

a. Use of WT for coefficient extractions or identification using wavelets.

2. To Characterize the ECG using wavelet a. Take WT of the ECG b. Detection of P , QRS and T waves of ECG

3. Analyze the extracted information by comparing with critical values4. Implement the neural network as a classifier for diagnosis of ECG

Wavelet TransformECG

Neural network

P,QRS, T Detection

ECG Characterization Diagnosis

Block Diagram of proposed work :

Wavelet :

• Powerful tool for Time-Frequency analysis• Helpful in interrogating non stationary signal• Decomposes Signal to single component more effectively than FFT

where a is scaling & b is translation

• WHY Wavelet ?– Transform localize the signal energy in both time and frequency– Can perform signal denoising and compression– Most suitable for non stationary signals like ECG

S T A R T

L O A D E C G S IG N A L

D E C O M P O S E E C G S IG N A L U P T O F O U R T H L E V E L

D E T E C T R P E A K

D E T E C T O N S E T A N D O F F S E T O F Q R S C O M P L E X

F IN D W ID T H O F Q R S C O M P L E X

D E T E C T P P E A K

D E T E C T O N S E T A N D O F F S E T O F P W A V E

F IN D W ID T H O F P Q S E G M E N T

D E T E C T T P E A K

D E T E C T O N S E T A N D O F F S E T O F T W A V E

Detection ProcessFlow Chart

A

END

FIND RR LENGTH ieHEART RATE

PRINT RR LENGTH, PQ & ST SEGMENTQRS WIDTH

FIND ST SEGMENT

A

Heart rate & Different Intervals:

•

• Biologically inspired networks• Useful in pattern recognition, classification etc.• Can be trained under supervised learning• Multilayer feed forward networks can be trained using back propagation algorithm

• Why NN :- Ability to learn- Improved accuracy in analyzing biomedical signals- Useful in classification and pattern recognition.

Neural Networks:

I/p node

Hidden layer

O/p nodes

•• ECG characterization :ECG characterization : Detection of P, QRS , and T parametersDetection of P, QRS , and T parameters

Diagnosis:Diagnosis:

Parameters extracted are used to diagnose cardiac problems like:Parameters extracted are used to diagnose cardiac problems like:

•• Atrial FibrillationAtrial Fibrillation•• Ventricular FibrillationVentricular Fibrillation•• Complete Heart blockComplete Heart block•• Pre ventricular contractionPre ventricular contraction•• left bundle branch blockleft bundle branch block•• normal sinus rhythmnormal sinus rhythm•• ischaemicischaemic / dilated / dilated cardiographycardiography•• sick sinus syndromesick sinus syndrome

FlowchartFlowchart ::

ECG records

Neural network classifier

P and T detection

WT /WPT

Local min/max detection

QRS detection

Diagnosis

References :References :

• Basics of ECG Reading : part I ”- www.nursce.com/courses/1071

• “ Using Wavelet Transforms for ECG characterization “- J. S. Sahambi, S. N. Tandon, R.K.P.Bhatt , Feb 1997,IEEE Engineering in medicine and biology.

• “ Application of Fuzzy neural network to ECG diagnosis “- Xie Zhi-Xing, Ning Xing –bao, 1997 IEEE

• “Wavelet packet transform applications on QRS detection “- Surapun Yimman , Kobachai Dejhan, 2004 IEEE

• “ Automatic ECG characteristic analysis using Wavelet Packet Transform “- Surapun Yimman , Kobachai Dejhan, 2006 IEEE

Thank You