final report new 1

DeclarationI hereby declare that this project report titled “ECG INTELLIGENT DECISION

MAKER” was executed as per the course requirement for the Bachelor’s Degree of

Technology in The Open University of Sri Lanka. No part of this has been

submitted by me or any other candidate for a Degree or a Diploma in any other

University or institution before. It is my own work.

Date: 26/07/2011

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AbstractAn Electrocardiogram is a bioelectric signal which record heart electrical activity

versus time. It is an important diagnostic tool for heart functioning. The

interpretation of ECG signal is an application of pattern recognition. The technique

used in this pattern recognition comprises: signal pre-processing, feature extraction,

neural network for classification.

This project consists with two parts because this is a group project.

The first part is an acquisition of ECG data from patients & record these data in the

application develop by C#.net & SQL Server. It does develop by My Colleague.

Second Part is Analysis the ECG record & gets the diagnosis result from trained

ANN. This report describes about the second part.

In this project wavelet transform and neural network toolbox are used from Matlab

environment. The processed signal is used from PhysioNet’s databases which were

developed for research in cardio electrophysiology.

An automated system for arrhythmia classification is developed. The system can

classify 3 different types of arrhythmia with an accuracy of 99.08%. These are

normal sinus rhythm, Right bundle branch block, left bundle branch block. Before

testing, the proposed structures are trained by back propagation algorithm. The

processed signal is used from MIT- Arrhythmia Database which is available in

PhysioNet’s database.

This part interconnects with main application & SQL server Database.

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Acknowledgments I would like to express my gratitude towards all the people who have contributed

their precious time and effort to help me. Without whom it would not have been

possible for me to understand and complete the project.

I would like to thank our Project Supervisor Eng. G.Anthonys, for him guidance,

support, motivation and encouragement throughout the period this work was carried

out. Him readiness for consultation at all times, him educative comments, him

concern and assistance even with practical things have been invaluable.

Last but not least, my utmost gratitude towards my family and loved ones for their

continuous support and encouragement.

Thank you all !!

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Table of ContentsDeclaration...................................................................................................................i

Abstract.......................................................................................................................ii

Acknowledgments.....................................................................................................iii

Table of Contents......................................................................................................iv

List of Figures...........................................................................................................vii

Chapter 1 Introduction..............................................................................................1

1.1 Project Background 1

1.2 Problem Statement 1

1.3 Project Aim and Objectives 2

Aim.......................................................................................................................2

Objectives.............................................................................................................2

1.4 Project Description 3

1.5 Organization of the Report 4

Chapter 2 - Project Literature Survey.....................................................................5

2.1 Review of Theory 5

2.1.1 ECG Signal & Characteristics.....................................................................5

2.1.2 Heart problems in this project......................................................................6

2.2 Review of Literature survey 8

2.2.1 Feature Extraction Methods.........................................................................9

2.2.2 Extraction Features to train ANN..............................................................12

Chapter 3 - Detail Design.........................................................................................14

3.1 Data Acquisition 14

3.1.1 Data Base...................................................................................................14

3.1.2 Signal Extraction........................................................................................14

3.2 Signal Pre-processing 15

3.2.1 Removal of the baseline drifts...................................................................15

3.2.2 Noise Filtering...........................................................................................16

3.3 Feature Extraction 17

3.3.1 Time Domain Features..........................................................................................17

3.3.2 Frequency Domain Features..................................................................................20

3.3.3 Feature Extraction Algorithm................................................................................21

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3.4 Build & Train the Neural Network 23

3.4.2 Train the network...................................................................................................26

3.4.3 Test the Neural Network........................................................................................26

Chapter 4 - Testing & Implementation..................................................................27

4.1 Toolboxes use to implement 27

4.2 Implementation Flow 27

4.2.1 Data Acquisition....................................................................................................27

4.2.2 Signal Pre-processing............................................................................................29

4.2.3 Feature Extraction 30

4.2.4 Train NN................................................................................................................31

4.2.5 Testing with real time data....................................................................................32

Chapter 5 - Results & Discussion............................................................................33

5.1 Results 33

5.1.1 Phase one – Training, Testing, Validation for pre-recorded data..........................33

5.1.2 Phase two – Testing Real Time Data..............................................................35

5.2 Specificity and Sensitivity 35

5.3 Discussions 36

5.3.1 Review of Theory & Approach.............................................................................36

5.3.2 Interpretation of Result..........................................................................................37

List of FiguresFigure 1.1-Overall project scope chart.........................................................................3

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Figure 2.1-Charcteristics of ECG.................................................................................5Figure 3.1-Flow Diagram of the system.....................................................................14Figure 3.2-MIT-BIH Database...................................................................................14Figure 3.3-The MIT-BIH Arrhythmia Database Records..........................................15Figure 3.4-Algorithm for Base line drift....................................................................16Figure 3.5-Algorithm for Noise removing.................................................................17Figure 3.6-Samples of Beat........................................................................................20Figure 3.7-Feature Extraction Algorithm...................................................................23Figure 3.8-Flow Diagram of ANN.............................................................................24Figure 4.1-ECG Prerecorded Data.............................................................................29Figure 4.2-DW Toolbox use for Denoise...................................................................31Figure 4.3-Matlab GUI feature Extraction.................................................................31Figure 4.4-Beat Samples a) Normal ,b) LBBB ,c) RBBB.........................................33Figure 4.5-Train ANN................................................................................................33Figure 4.6-Real time ECG Diagnosis GUI.................................................................33Figure 5.1-Performance of ANN................................................................................34Figure 5.2-Training State of ANN.............................................................................35Figure 5.3-Confusion matrix of ANN........................................................................35Figure 5.4-Specificity & Sensitivity of ANN.............................................................37

List of Tables

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Table 2.1-Duration values & intervals for normal arrhythmia.....................................6Table 2.2-Comparison of Different Feature Extraction Techniques..........................12Table 3.1-ECG Wave Time Domain Feature Extraction...........................................18Table 3.2-Input Neurons of the ANN.........................................................................22Table 3.3-The output target vector of ANN...............................................................23Table 4.1-Use Toolboxes...........................................................................................25Table 4.2-ECG Records from MIT/BIH....................................................................26Table 4.3-Functions of Main Matlab GUI.................................................................29Table 5.1-Performance comparison of proposed Multi-ANN classifier....................35

List of Abbreviation

vii

ANN - Artificial Neural Network

BP - Back Propagation

DWT - Discreet Wavelet

ECG - Electro Cardio Graph

FNN - Fuzzy Neural Network

GUI - Graphical User Interface

LBBB - Left Bundle Branch Block

LVD - Left Ventricular Dimension

LVQ - Learning Vector Quantization

ML - Multi Layer

MLP - Multi Layer Perception

NN - Neural Network

NSR - Normal Sinus Rhythm

PB - Paced Beat

PCA - Principal Component Analysis

RBBB - Right Bundle Branch Block

SOM - Self Organizing Map

SVW - Slope Vector Waveform

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ECG Intelligent Decision Maker

Chapter 1 - Introduction

1.1 Project Background

This project is a group project with two main parts. First part includes ECG gaining

hardware. It can get the ECG signal from the patient & analyze the signal. This part

has application develop by SQL SERVER & C#.net. Second part includes Intelligent

ECG analyzing system. This part consists with Signal processing techniques and

artificial neural network (ANN).In this part ANN train first using online databases.

After test the ANN using real time data get from the ECG hardware.

Our overall system will be used to implement a real time processing, intelligent, cost

effective, and easy-to-use ECG diagnostic system. It also gives suggestion to improve

the experiments and use of remote diagnostic medical systems for diagnosing at

homes in the future.

1.2 Problem Statement

Technological innovation has progressed at such an accelerated pace that it is has

permeated almost every facet of our lives. This is especially true in the field medicine

and delivery of health care services. As a result, AI technologies attached to the

biomedical experiments with better investing of more advanced technologies for best

monitoring of patient.

In Sri Lanka, cardiac disorders are common among the people but technology has not

improved with cardiac signal diagnosis technologies. Still Bioinformatics’

technologies and AI are rarely used for ECG signal diagnosis.

So Personnel Intelligent ECG Analyzing System is timely required with computerized

world to quick and accurate predictions which could be help to cardiologist to prevent

death amount by cardiac problems. This project idea is for making ECG Advisor

Intelligent software by using Signal Processing, Feature extraction, Neural

Networking, fuzzy logic and DB management technologies.

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1.3 Project Aim and Objectives

Aim

The aim of this overall project is to create ECG Signal analyzing system for diagnosis

decision making regarding to their health state without involving a doctor.

Objectives

Implementation a suitable method for the ECG features extraction.

Analyzing suitable features for improving the performance of diagnosis.

Build the best c architecture for the ECG signal classification.

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1.4 Project Description

The overall project has two main parts. Real time ECG Acquisition & Diagnosis the wave in

intelligently. Figure 1.1 shows the overall project scope.

Figure 1.1-Overall project scope chart

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1.5 Organization of the Report

In the second chapter I construe the basic principles of electrocardiogram

(ECG) which includes ECG Signal & Characteristics, Heart problems in this

project, literature survey of Feature Extraction Methods & researches done.

In the third chapter we discuss the detail design of the project.

The fourth chapter deals with implementation & testing of the design

In the last chapter I discuss the Results of the implementation & discussion,

conclusion and future works of the project.

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Chapter 2 - Project Literature Survey

2.1 Review of Theory

2.1.1 ECG Signal & Characteristics

Understanding the Electrocardiography and its characteristics is one of the most

important aspects during the development of this project. Electrocardiography or ECG

in short, is the most commonly used and direct method for assessing abnormalities of

cardiac rhythm. As shown in Figure 1 below, ECG tracing of the cardiac cycle

typically consists of a P wave, a QRS complex, and a T wave .

Figure 2.2-Charcteristics of ECG

ECG provides us with two types of information. First is the duration of electrical

wave crossing the heart, which is measured by measuring the time intervals on the

ECG. This will determined the regularity of the electrical activity, if it is normal,

slow, fast or irregular. Second is the amount of the electrical activity passing through

the heart muscle and is represented by the principal components (peaks and valleys)

of the ECG. Arrhythmia usually can be analyzed based on the change of the rhythm or

frequencies of the heartbeat. The normal values for duration of ECG waves and

intervals in adults were presented in the Table 2.1.

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Table 2.1-Duration values & intervals for normal arrhythmia

ECG Wave

Type

Description Time and

Amplitudes

P wave During normal atrial depolarization, the main

electrical vector is directed from the SA node

towards the AV node, and spreads from the right

atrium to the left atrium. This turns into the P wave

on the ECG.

0.25mV

0.08S

QRS

complex

The QRS complex is a recording of a single

heartbeat on the ECG that corresponds to the

depolarization of the right and left ventricles.

1.60mV

0.08S- 0.12S

PR interval The PR interval is measured from the beginning of

the P wave to the beginning of the QRS complex.

0. 12S to 0.2S

ST segment The ST segment connects the QRS complex and the

T wave.

0.08S to 0.12S

ST interval This represent the duration of J point to T on set 0.32S

T wave The T wave represents the repolarization (or

recovery) of the ventricles.

0.1mV to 0.5mV

0.16S

Q wave 0.4mV

QT interval The QT interval is measured from the beginning of

the QRS complex to the end of the T wave.

0.3S to 0.43S.

2.1.2 Heart problems in this project

Changes from the normal morphology of the electrocardiogram can be used to

diagnose many different types of arrhythmia or conduction problems. The

electrocardiogram can be split into different segments and intervals, which relate

directly to phases of cardiac conduction; limits can be set on these to diagnose

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http://en.wikipedia.org/wiki/QT_interval

http://en.wikipedia.org/wiki/Atrium_(anatomy)

http://en.wikipedia.org/wiki/Atrium_(anatomy)


abnormality. The physician normally uses the ECG and other factors to determine the

gross condition of the heart.

There are a lot of heart problems, which can be diagnosed from different ECG

waveforms. This project aims at classifying 5 different ECG waveforms from the

database. They are:

Normal (N)

Paced beats (P)

Right bundle branch block (R)

Left bundle branch block (L)

Fusion of paced (F)

They are explain as follows,

Paced beats

This is the artificial beat from the device called pacemaker. A pacemaker is a

treatment for dangerously slow heart beats. Without treatment, a slow heart beat can

lead to weakness, confusion, dizziness, fainting, shortness of breath and death. Slow

heart beats can be the result of metabolic abnormalities or occur as a result of blocked

arteries to the heart's conduction system. These conditions can often be treated and a

normal heart beat will resume. Slow heart beats can also be a side effect of certain

medications in which case discontinuation of the medicine or a reduction in dose may

correct the problem.

Right bundle branch block

Right bundle branch block (RBBB) can be suggested by the following ECG

characters:

1) The QRS duration between 0.10 and 0.11sec (Incomplete RBBB) or 0.12sec.or

more (complete RBBB);

2) Prolonged ventricular activation time or QR interval (0.03sec. or more in V1-V2);

3) Right axis deviation.

Incomplete RBBB often produce patterns similar to those of right ventricular

hypertrophy. The ECG pattern of RBBB is frequently associated with ischemic,

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hypertensive, rheumatic and pulmonary heart disease, right ventricular hypertrophy

and some drug intoxication; occasionally it may be found in healthy individuals.

Left bundle branch block

1. The heart rhythm must be supraventricular in origin

2. The QRS duration must be = or > 120 ms

3. There should be a QS or rS complex in lead V1

4. There should be a RsR' wave in lead V6.

Fusion of paced and normal beats

Ventricular fusions beats occur when impulses from an ectopic ventricular focus and

supraventricular pacemaker simultaneously activate the ventricular pacemaker

simultaneously activate the ventricules; the resulting QRS complex is of shorter

duration as compared to those of ventricular origin. Ventricular tachycardia is caused

by rapid and regular discharge in an ectopic focus in any portion of the ventricular

myocardium. Consequently, the ECG shows the following characteristic features:

1. A heart rate between 140 and 200 per minute; occasionally the rate may be

slightly slower or faster;

2. Wide and bizarre QRS complexes;

3. Regular or slightly irregular RR intervals;

4. P waves of sinus or atrial origin occurring at a slower rate (60-14);

5. A P wave bears no fixed relationship to the QRS complex.

Ventricular tachycardia has a premature onset and its termination is followed by a

pause. When the rate is below 150 per minute, the tracing may show ventricular

capture beats or ventricular fusion beats characterized by a narrow QRS complex.

2.2 Review of Literature survey

When I do the literature survey of this project, I found the most critical point is

Feature extraction. Therefore mainly my attention has goes to the Literature Survey of

feature Extraction Methodologies and most suitable features of the train ANN.

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2.2.1 Feature Extraction Methods

The proposed schemes were mostly based on Fuzzy Logic Methods, Artificial Neural

Networks (ANN), Genetic Algorithm (GA), Support Vector Machines (SVM), and

other Signal Analysis techniques. All these techniques and algorithms have their

advantages and limitations. This chapter discusses various techniques and

transformations proposed earlier in literature for extracting feature from an ECG

signal. In this chapter also provides a comparative study of various methods proposed

by researchers in extracting the feature from ECG signal.

Feature extraction methods literature

Author: Zhao et al. [1]

Techniques: Wavelet transform, Support vector machines

Method: The wavelet transform is used to extract the coefficients of the transform as

the features of each ECG segment. Concurrently, autoregressive modeling (AR) is

also applied to get hold of the temporal structures of ECG waveforms. Then at last the

support vector machine (SVM) with Gaussian kernel is used to classify different ECG

heart rhythm.

Accuracy: 99.68%.

Author: Mahmoodabadi et al. [2]

Techniques: Daubechies Wavelets transform.

Method: De-noise the ECG signal by removing the equivalent wavelet coefficients at

higher scales. Then, QRS complexes are detected and each one complex is used to

trace the peaks of the individual waves, including onsets and offsets of the P and T

waves which are present in one cardiac cycle

Accuracy: Sensitivity -99.18%

Positive predictivity -98%.

Author: Tadejko and Rakowski [3]

Techniques: SOM

LVQ

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Method: The obtained feature sets were based on ECG morphology and RR-intervals.

The mathematical morphology filtering is used for the preprocessing of ECG signal.

In addition their work compares two strategies for classification of annotated QRS

complexes.

Author: Tayel and Bouridy [4]

Techniques: Wavelet transformation

Neural networks

Method: Features are extracted from wavelet decomposition of the ECG images

intensity. The obtained ECG features are then further processed using artificial neural

networks. The features are: mean, median, maximum, minimum, range, standard

deviation, variance, and mean absolute deviation. The introduced ANN was trained by

the main features of the 63 ECG images of different diseases.

Accuracy: 92%.

Author: Xu et al. [5]

Techniques: SVW

Method: variable stage differentiation is used to achieve the desired slope vectors for

feature extraction, and the non-linear amplification is used to get better of the signal-

to-noise ratio. The method allows for a fast and accurate search of the R location,

QRS complex duration, and RR interval and yields excellent ECG feature extraction

results.

Author: Alexakis et al. [6]

Techniques: ANN

LDA

Method: Five ECG features namely RR, RTc, T wave amplitude, T wave skewness,

and T wave kurtosis were used in their method. These features are obtained with the

assistance of automatic algorithms. The onset and end of the T wave were detected

using the tangent method. The three feature combinations used had very analogous

performance when considering the average performance metrics.

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Author: Saxena et al. [7]

Techniques: Quadratic spline wavelet (QSWT)

Daubechies six coefficient (DU6)

Method: Two wavelets have been used, Quadratic spline wavelet (QSWT) for QRS

detection and the Daubechies six coefficient (DU6) wavelet for P and T detection. A

procedure has been evolved using electrocardiogram parameters with a point scoring

system for diagnosis of various cardiac diseases. The consistency and reliability of the

identified and measured parameters were confirmed when both the diagnostic criteria

gave the same results.

Author: Jen et al. in [8]

Techniques: Cepstrum coefficient method

ANN

Method: One can identify the characteristics hiding inside an ECG signal and then

classify the signal as well as diagnose the abnormalities. To explore the performance

of the proposed method various types of ECG data from the MIT/BIH database were

used for verification.

Accuracy: 97.5%.

Author: Ramli and Ahmad in [9]

Techniques: Correlation analysis

Method: Their proposed work investigated the technique to extract the important

features from the 12 lead system (electrocardiogram) ECG signals. They chose II for

their entire analysis due to its representative characteristics for identifying the

common heart diseases. The analysis technique chosen is the cross-correlation

analysis. Cross-correlation analysis measures the similarity between the two signals

and extracts the information present in the signals. Their test results suggested that the

proposed technique could effectively extract features, which differentiate between the

types of heart diseases analyzed and also for normal heart signal.

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2.2.2 Comparison of Different Feature Extraction Techniques

Below table 2.2 shows the comparison of the different feature extraction techniques

from an ECG Signal.

Table 2.2-Comparison of Different Feature Extraction Techniques

Approach Simplicity Accuracy Predictivity

Zhao et al. H H H

MahmoodabadiM H H

et al.

Tadejko andL M M

Rakowski

Tayel andM M H

Bouridy

Jen et al.

H H H

Alexakis et al.

H M M

Ramli andM M M

Ahmad

Xu et al.M H H

H- High, L-Low, M-Medium

2.2.2 Extraction Features to train ANN

The main objective of the feature extraction process is to derive a set of parameters

that could best characterize the signal. These parameters, in other words, should

12


contain maximum information about the signal. Hence the selection of these

parameters is an important criterion to be considered for proper classification.

Arrhythmia classification, therefore, involves determination of several characteristic

features of the ECG signal.

In my literature survey I found two categories of features,

Time domain features

The time intervals of the characteristic waves of the ECG signal are used for

calculating a set of time domain parameters.

Frequency domain features

Samples of ECG wave

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Chapter 3 - Detail Design The algorithm proposed in this project is designed towards detecting of arrhythmia

based on ANN that has been build from ECG wave sample detection & fiducial point

detection, with the prerecorded and Real time ECG datasets. The algorithm applied is

mainly based on the theory and observation made on similarities of ECG wave-shapes

& ECG features that are presented in the same cardiovascular diseases. The flow of

the system & model of this algorithm is presented in Figure 3.1, and Figure 3.2.

Figure 3.3-Flow Diagram of the system

Figure 3.4-Decsion Maker algorithm

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Data Acquisition

Signal Pre-

processing

Feature

Extraction

Build & Train

Neural Network

Test Neural Netwo

rk


3.1 Data Acquisition

3.1.1 Data Base

I selected the MIT-BIH database to base my work on because it is available online

where the ECG files are easily accessible and because many important works in the

literature have based their work on this database and have reported their results and

findings in published and widely acknowledged papers.

Figure 3.5-MIT- BIH Database

3.1.2 Signal Extraction

As can be seen in Figure 3.2, the MIT-BIH Arrhythmia Database provides three set of

files to be downloaded for each record. The .dat files are binary signal files and

contain the actual data like the number of sample and the amplitude of the signal at

that sample point. The .hea files are short text “header” files used to determine the

location and format of the signal files by the software that reads them. Finally, the .atr

files are binary files containing annotations or labels pointing to specific locations

within the signals it is clearly stated at the PhysioBank that simply saving

the .dat, .hea, and .atr of each record would not enable the user to view the signals and

process them further.

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Figure 3.6-The MIT-BIH Arrhythmia Database Records

3.2 Signal Pre-processing

ECG signal mainly contains noises of different types, namely frequency interference,

baseline drift, electrode contact noise, polarization noise, muscle noise, the internal

amplifier noise and motor artifacts. Artifacts are the noise induced to ECG signals

that result from movements of electrodes. One of the common problems in ECG

signal processing is baseline wander removal and noise suppression.

3.2.1 Removal of the baseline drifts

Baseline wandering is one of the noise artifacts that affect ECG signals. Removal of

baseline wander is therefore required in the analysis of the ECG signal to minimize

the changes in beat morphology with no physiological counterpart. Respiration and

electrode impedance changes due to perspiration are important sources of baseline

wander in most types of ECG recordings. The frequency content of the baseline

wander is usually in a range well below 0.5Hz. This baseline drift can be eliminated

without changing or disturbing the characteristics of the waveform.

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Load Signal .mat (S1)

Smooth signal (S2)

Out signal= (S1) - (S2)

Out signal

1


Figure 3.7-Algorithm for Base line drift

3.2.2 Noise Filtering

After removing baseline wander, the resulting ECG signal is more stationary and

explicit than the original signal. However, some other types of noise might still affect

feature extraction of the ECG signal. In order to reduce the noise many techniques are

available like Digital filters, Adaptive method and Wavelet Transform thresholding

methods. Digital filters and Adaptive methods can be applied to signal whose

statistical characteristics are stationary in many cases. However, for nonstationary

signals it is not adequate to use Digital filters or Adaptive method because of loss of

information. To remove the noise, we use Discrete Wavelet transform.

17

1

Wavelet decomposition at level 8

Extract the coefficients

Default values for de-noising in Wavelet

Signal obtained by wavelet coefficients thresholding using global positive threshold


Figure 3.8-Algorithm for Noise removing

3.3 Feature Extraction

ECG Feature Extraction plays a significant role in diagnosing most of the cardiac

diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. This

feature extraction scheme determines the amplitudes and intervals in the ECG signal

for subsequent analysis. The amplitudes and intervals value of P-QRS-T segment

determines the functioning of heart of every human. In feature extraction I used Time

Domain features & Frequency Domain features.

3.3.1 Time Domain Features

According to the literature survey of this project, I identify 12 main features to

extraction from the ECG wave. Also analyze the suitable methods to identify these

features. Below table summarize the Feature, Method use to extract value & graphical

view of the feature.

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Table 3.3 -ECG Wave Time Domain Feature Extraction

Feature name Method View

R peak amplitude The R-peak detection was

carried out by using the Pan,

Tompkins method of R peak

detection. The method uses

the threshold level to

calculate the maximum

amplitude in the ECG

waveform.fig(1)

Heart Beat Rate By taking the difference in

time between consecutive R-

peaks.Fig(2)

Q wave amplitude After detecting R-peak,

knowing the QRS complex

duration to be 60ms, points Q

was estimated as the first

inflection point to the left of

R-peak.

Fig(1)

T wave amplitude T-peak was estimated to

between R-peak+400ms to J-

point+80ms

Fig(2)

P wave amplitude P-point was estimated to be

the first inflection point after

K-point on the P-peak side

Fig(2)

T wave Duration Toffset-Tonset

Fig(3)

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QRS Duration Point S was estimated as the

first inflection point to the

right of R-peak.

Fig(3)

ST Duration T offset- S onset

Fig(4)

PR Duration R offset-P onset Fig(4)

P Wave Duration P offset-P onset Fig(4)

Cycle duration T offset-P onset Fig(4)

Auto Correlation

Coefficient

Getting two points with

adjacent to each other till the

end point.

Correlation Cofficient=∑ ( x−x )( y− y )

√∑ ( x−x )2∑ ( y− y )2

20

fid_index – [X] fid_index + [Y]

ECG signal divided into windows with length of one cardiac cycle

A simulation representation of all the cardiac cycle segmented overlapping one another

begin_pt end_ptfid_index


3.3.2 Frequency Domain Features

In feature extraction of the ECG, I used No. of samples in the each cardiac cycle in

given time period. In this method I extract the 252 samples per each cardiac cycle.

Figure 3.9-Samples of Beat

First detect the R points of the full wave after that remove the first & last beats

according to R points. Remain signal cut the samples per + 0.35, R,-0.35.It gives the

number of samples of each time period.

3.3.3 Feature Extraction Algorithm

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22


23


Figure 3.10-Feature Extraction Algorithm

3.4 Build & Train the Neural Network

In this stage I use the ANN to classify the data, the ANN train to Identify Normal,

Abnormal, RBBB, LBBB & Paced beat.

3.4.1 Build ANN

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To build ANN I follow the below design steps,

Figure 3.11-Flow Diagram of ANN

Assemble the training data

In order to diagnose heart conditions using neural network technique, the most

common ECG waveforms were selected from the MIT/BIH database. The selected

ECG waveform which will be used in this project are divided into four categories,

namely

Normal (N)

Abnormal(AN)

Right bundle branch block (R)

Left bundle branch block (L)

Twenty two different patients, who have been considered in the experiments (taken

from

MITBIH),100,101,103,105,106,109,111,113,115,116,118,119,122,123,124,200,203,2

07,208,212,214,215.

Design the Neural Network Architecture

A 264-element input vector is defined as a matrix of input vectors for one sample to

compare the neural network performance. The input vector is defined for each

heartbeat and the corresponding element in the target vector is also defined with a

combination of 1s or 0s to represent each of the class. Output vectors include 5-

vectors. The network is required to classify 5 heart conditions by responding with this

output vector. Each output vector represents one of 5 classes.

Input Layer

Input payer include 264 neurons, like as below,

25

Assemble the

training data

Create the architecture of Neural Network

Train the network

Simulate the network response to new inputs


Table 3.4-Input Neurons of the ANN

Feature Type Source Description

1 Morphological features P,T waves Cycle Duration

2 Morphological features P, R waves P-R interval

3 Morphological features QRS complex QRS Duration

4 Morphological features R waves R amplitude

5 Morphological features R waves HBR

6 Morphological features P wave P wave Amplitude

7 Morphological features T wave T wave Amplitude

8 Morphological features Q wave Q wave Amplitude

9 Morphological features S-T waves S-T Interval

10 Morphological features T wave T wave Duration

11 Morphological features P wave P wave Duration

12 Statistical features Correlation

Coefficient

Auto Correlation

Coefficient

13-264 Statistical features Number of samples Number of samples

on the ECG beat of

the given time period

26


Output Layer

Target vectors of the NN shown in the Table 3.3,

Table 3.5-The output target vector of ANN

Signal Vector Heat condition

N [1 0 0 ] Normal

AN [0 1 0] Abnormal

L [0 0 1] Left bundle branch block

R [0 1 0 ] Right bundle branch block

3.4.2 Train the network

The Levenberg-Marquardt backpropagation was used to train the multi-layer

feedforward backpropagation networks. The training data sets were selected from the

most common ECG waveforms. Five classes with 100 time episodes in each were

selected to train the networks.

Training algorithm

The Levenberg-Marquardt backpropagation was used to train the multi-layer

feedforward backpropagation networks. It is very fast but requires a lot of memory to

run. For each epoch, if the network performance decreases toward the goal, the

learning rate is increased. If the performance increases, the learning rate is then

adjusted and the change, which increased the performance, is not made.

3.4.3 Test the Neural Network

To test the neural network I use 15% data of the overall Data selection in MIT/BIH

data base & real time data acquisition from ECG hardware.

27


Chapter 4 - Testing & ImplementationIn this chapter represent the implementation of my design, discussed in the previous

chapter. Also describes how I test implementation using data & tools.

4.1 Toolboxes use to implement

Table 4.6-Use Toolboxes

Toolbox Name Purpose

MIT/BIH Arrhythmia Database To data acquisition

Matlab Wavelet Toolbox To DWT DB4 wavelet of ECG wave.

Matlab Guide Create GUI

Matlab Data base Toolbox To connect with Microsoft SQL server

and for execute the Queries of SELECT

and UPDATE

4.2 Implementation Flow

4.2.1 Data Acquisition

Step 01: Download each signal from MIT/BIH Arrhythmia database.

I downloaded 21 signals from mit/bih aryythmia database. Signal duration is 30

minutes. Segmented each signal in to 10s periods.

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Figure 4.12-ECG Prerecorded Data

Step 02: Analyze annotations

Analyze the annotation of the every 10s segment & list them like as below,

Table 4.7-Analyze Annotations

Using above list, I have limited the number of patients, taking part in the experiments,

to provide at least different arrhythmia cases (N, R and L).

Table 4.8-ECG Records from MIT/BIH

Records Normal RBBB LBBB

Patient 100 62 0 0

Patient 101 5 0 0

Patient 103 58 0 0

Patient 105 10 0 0

Patient 106 27 0 0

Patient 109 0 0 104

Patient 111 0 0 41

Patient 113 6 0 0

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Patient 115 10 0 0

Patient 116 45 0 0

Patient 118 0 12 0

Patient 119 50 0 0

Patient 122 5 0 0

Patient 123 5 0 0

Patient 124 0 33 0

Patient 200 0 0 0

Patient 203 0 0 0

Patient 207 0 0 40

Patient 208 0 0 0

Patient 212 5 26 0

Patient 214 0 0 50

Patient 215 103 0 0

391 71 235

4.2.2 Signal Pre-processing

Step 03: Discrete wavelet Analysis

After download ECG file in .mat format, start the preprocessing stage. In

preprocessing stage use the Wavelet toolbox .Load the data choose the wavelet type

db-4,level 8.It has given the signal like as below,

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Figure

4.13-DW Toolbox use for Denoise

4.2.3 Feature Extraction

To extract features I develop the Matlab GUI, using algorithm previous chapter

described.

a). Extract Time –Domain Features

Figure 4.14-Matlab GUI feature Extraction

The extraction features save to the excel sheet.

Functions

Table 4.9-Functions of Main Matlab GUI

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Function Description

Load Data Load the .mat data we need to extract feature values.

Analysis Identify peaks, & extract features

Save Save features to the excel sheet.

b).Extract Frequency Domain Feature

I get the samples in each wave using the frequency, samples in the three diagnoses

shown in Figure 4.4,

(a) (b)

(c)

Figure 4.15-Beat Samples a) Normal , b) LBBB , c) RBBB

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4.2.4 Train NN

To train ANN I used 2136 samples to both ANN. Here is the training stage result,

Hidden layer has 20 neurons.

Figure 4.16-Train ANN

4.2.5 Testing with real time data

In finally I have to interconnect my trained NN with ECG Advisor front end

application. Acquisition of the real time ECG wave from patient & give the Diagnoses

the result to the SQL Database. In Figure shows GUI, that’s connect with front end

application,

Figure 4.17-Real time ECG Diagnosis GUI

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Chapter 5 - Results & Discussion

5.1 Results

5.1.1 Phase one – Training, Testing, Validation for pre-recorded data

In this stage I trained ANN using several data sets in below,

Samples data set

Time Domain Data set

Time Domain + Samples data set

From above data sets I got the maximum result in Time Domain + Samples data set.

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Figure 5.18-Performance of ANN

Figure 5.19-Training State of ANN

Figure 5.20-Confusion matrix of ANN

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5.1.2 Phase two – Testing Real Time Data

When I test the real time ECG wave, I have to vary sampling frequency according to

the wave sampling ratio, & wave taken time.

Table 5.10-Testing accuracy of real time data

5.2 Specificity and Sensitivity

Specificity refers to the measurement of the proportion of negatives, which have been

detected. Sensitivity, on the other hand, measures the proportion of actual positives

that have been correctly identified.

36

File Name Duration(s) Percentage

Classify (%)

P1 10 100%

P2 11.1 90%

P3 12 93%

P4 10 80%

P5 10.6 94%

Overall

Accuracy

91.4%


Figure 5.21-Specificity & Sensitivity of ANN

5.3 Discussions

Our target was to detect a lot of ECG signals and to recognize ECG arrhythmias. In

this study in which ANN was employed as the basic method accuracy rates were

appeared well than similar studies. Moreover, number of arrhythmias can be increased

in the ECGAnalyzer Software.

Therefore, lots of patients’ ECGs can be investigated and interpreted in a very short

time. This will improve the performance of heart treatment. These studies divided two

major groups, in terms of the data used. Those are MITBIH ECG arrhythmia database

and Real time data. It can easily be observed that our study is more recognized than

other studies using MIT-BIH database. Moreover we used data obtained from patients

together with real time data.

37


5.3.1 Review of Theory & Approach

The approach taken in this project is based on the observation of ECG arrhythmias on

patients with the neural network. To recap, the algorithm applied involved with

feature extraction of the ECG, and build the NN using mean of every fiducially point

on the cycle segmented. NSR, RBBB, LBBB, ECG were tested using trained NN, and

determined the diagnosis result based on the parameters observed, such as the

intervals, fiducial points amplitudes, and Statically features. The testing phase was

divided into two phases testing with pre recoded data and testing with real time data.

5.3.2 Interpretation of Result

In each of the phases, based on the observation of the percentage ANN Training,

Testing & Validation, the results appeared to be better than expected. For most of the

result presented, the percentages are exactly 100% matched, which is slightly higher

than the minimum percentage set in the algorithm (i.e. >70%). The specificity,

sensitivity and accuracy, however, still display reasonably good results. Even though

the specificity and accuracy is lower, especially when compared to results from other

relevant studies/journals) the algorithm is still capable to detect Normal, Abnormal &

several diagnosis in random ECG obtained from the Physionet database & real-time

data , which has been further proven during test result (Table 5.2). By achieving this

result, the target of this project has been achieved.

Table 5.11-Performance comparison of proposed Multi-ANN classifier

Reference

Classifier

Classification Accuracy

(%)

Rajendra et al (2003) ANN and Fuzzy 95.00

Rajendra et al (2004) ANN and Fuzzy 87.00

Obayya et al (2008) Hidden Markov

Models

98.18

38


Kannathal et al (2005) Adaptive Neuro Fuzzy 94.64

Proposed ANN 99.08

5.3.4 Problems Encountered

There are a few concerns relating to the feature extraction algorithm. One of the main

concerns is its capability to detect relative diagnosis in the presence of other

arrhythmia, abnormalities or stresses, within the same ECG. In this project, the main

focus is to be able to detect 4 arrhythmias, and the datasets used were mainly NSR,

RBBB, LBBB and PB ECG annotated. Due to lack of time, and database/resources

accessibility, no test was done for cases other than the above mentioned. Thus it is

unknown if the algorithm would be able to detect correctly or able to differentiate

between different arrhythmia, such as premature atrial contraction or multifocal atrial

tachycardia.

Another issue encountered was related to the detection of the R-peaks. Most of the

ECG datasets used did not present any problems, except for a few cases. In these

cases, based on observation, their R-peaks were generally quite low. The only way to

get them detected is to manually change the threshold values. However, upon testing,

it is quite impossible to modify the values without compromising the results for the

majority of the ECG datasets. Hence, this shows that the thresholds implemented for

the R-peak detection were not adaptive or dynamic enough to accommodate to this

scenario.

Another problem we faced when train the NN, arrhythmias RBBB, LBBB features are

show the close values. Therefore NN performance has gone to low.

5.4 Conclusion & Future work

The purpose of the project was to design an ECG Analyzer that takes human ECG

data, and provides a diagnosis for the state of the heart. The project was composed of

two main components: Feature Extraction and Train ANN.

39


5.4.1 Conclusion

In this report, I have presented an algorithm for ANN diagnosis of ECG extracted

from digitized ECG database consisting of NSR, RBBB and LBBB recordings. The

idea of this algorithm was based on the theory studied that patients with same cardiac

arrhythmias/abnormalities tend to have similarities in their ECG. This observation,

however, was made by factoring out a number of morphological variations such as

variability that could affect the ECG amplitude level.

The results gathered was better than expected, especially with the algorithm’s

sensitivity, whereby more than 80% of the overall results were correctly identified as

positive. Further improvement could be made, however, as mentioned in the previous

chapter, there were issues that are yet to be resolved. The concerning issues are

involved with the algorithm’s incapability of detecting R-peaks that are lower than the

ones tested and found in a typical ECG, as well as the uncertainty of the outcome or

result if other types of arrhythmia were present in the datasets.

However, despite the problems encountered, I would conclude that the project is still

quite a success, since the objective of developing an algorithm to detect arrhythmia

has been achieved.

5.4.2 Recommendation for Future Work

There are few things I would like to recommend for future work. One of the things

will be improving the algorithm to detect more arrhythmias on real-time. The method

applied in this project is mainly focused on few ECG arrhythmias. To be able to do

this we have to extract additional advanced features of ECG.

Another improvement that could be made is to use fuzzy logic to smooth the answer

using beat by beat classification in NN.

40


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http://hdl.handle.net/123456789/184





APPENDIX ATrain ANN

a).Load Data

b).Pre –Process

Correct Base Line

De-noise the signal

A1


c).Detect R-Peaks

d) Cut samples

e) Extract Features

A2


A3

final report new 1

Documents