digital signal processing of ecg signals to diagnose heart

Digital Signal Processing of ECG signals to Diagnose Heart

Diseases

by

Asrith Krishna Chakka

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in

Microelectronics and Embedded systems

Examination Committee: Dr. Mongkol Ekpanyapong (Chairperson)

Assoc. Prof. Erik L.J. Bohez

Dr. A.M. Harsha S. Abeykoon

Nationality: Indian

Previous Degree: Bachelor of Technology in

Electronics and Communications Engineering

Jawaharlal Nehru Technological University Hyderabad

Telangana, India

Scholarship Donor: AIT Fellowship

Asian Institute of Technology

School of Engineering and Technology

Thailand

December 2017

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ACKNOWLEDGEMENTS

I, Asrith Krishna Chakka, would like to profusely thank my advisor, Dr. Mongkol

Ekpanyapong, for his generous gratitude, attentive support and interest in this field. Besides,

he even recommended me about the special study in the topic of “ECG Signals to Diagnose

Diseases” which is used here in thesis.

I would also like to extend my gratitude and sincere thanks to Dr. A. M. Harsha S Abeykoon

and Assoc. Prof. Erik L.J. Bohez for their kind support, guidance and willingness to serve as

the examination committee members.

Furthermore, I would like to thank my family for the support they have extended, without

which this thesis would not have been possible.

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ABSTRACT

As per the data given by WHO many people die due to heart diseases and it is considered as

the second greatest killer. So, any upgradations or improvements in treatments or diagnosis

tools would be of great help to the society and are most encouraged in the medical field.

Electrocardiogram (ECG) is considered as the most useful tool for diagnosing heart patients.

This is operated by recording the electrical signals that heart emits and these emitted signals

are recorded using electrodes that are placed on the chest and limbs. The main purpose of the

project is to classify heart diseases by digital signal processing of ECG signals and

diagnosing those diseases. Those diseases that can be classified by digital signal processing

of ECG signals include arrhythmia, heat block, cardiomyopathy, bundle branch block etc. So,

our main objective is to classify some of these diseases and diagnose them. Pan Tompkins

algorithm is used for the purpose of detecting QRS complex and discrete wavelet transform

for wave decomposition, for classifying these signals we used k- Nearest Neighbor algorithm.

Using MATLAB these ECG signals are digital signal processed. ECG signals that are used in

this paper are obtained from PTB Diagnostic database of physionet.org.

Keywords: ECG (Electrocardiogram), Pan Tompkins algorithm, DWT (Discrete

Wavelet Transform), k-NN classifier, PTB database.

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TABLE OF CONTENTS (Cont’l)

CHAPTER TITLE PAGE

TITLE PAGE i

ACKNOWLEDGEMENTS ii

ABSTRACT iii

TABLE OF CONTENTS iv

LIST OF FIGURES vi

LIST OF TABLES vii

LIST OF ABBREVATIONS ix

1. INTRODUCTION 1

1.1. Background 1

1.2. Statement of the problem 2

1.3. Objectives 2

1.4. Scope and limitations 3

1.5. Research Outline 3

2. LITERATURE REVIEW 4

2.1. Review 4

2.2. Detection and Classification of heart diseases 4

2.2.1. Early prediction of cardiovascular diseases using ECG Signals 4

2.2.2. Classification of Cardiac vascular disease from ECG signals 5

2.2.3. Removal of noise from ECG signals using digital 7

Signal Processing

2.2.4. ANN based Arrhythmia Diagnosis 8

2.2.5. Diagnosis of CAD using nonlinear features extracted from 10

ECG signals

2.2.6. Classification of Heart diseases using k-NN and genetic 11

algorithm

2.2.7. Medical Decision Support System for Diagnosis of 13

Cardiovascular Disease using DWT and k-NN

2.2.8. Diagnosing of heart disease patients by applying kNN 14

2.2.9. QRS Detection using k-Nearest Neighbor algorithm (KNN) 15

and Evaluation of standard ECG database

2.2.10.Performance analysis of ANN for cardiac arrhythmia detection 17

2.3. Diseases Diagnosed 17

2.3.1. Cardiomyopathy 17

2.3.2. Myocardial Infarction 18

2.3.3. Dysrhythmia 19

2.3.4. Bundle branch block 20

2.3.5. Healthy ECG signals 21

3. METHODOLOGY 23

3.1. Databases 23

3.2. Pre-processing 24

v

3.3. Features Extraction 24

3.3.1 Pan Tompkins algorithm 24

3.3.2 Discrete Wavelet Transform 26

3.4. Classification of ECG signals 27

3.4.1 Classification using k-NN 27

3.5 Process of Execution 28

3.6 Comparison 29

4. RESULTS 30

4.1 Testing of Cardiomyopathy 30

4.2 Testing of Myocardial Infarction 35

4.3 Testing of Dysrhythmia 40

4.4 Testing of Bundle branch block 45

4.5 Testing of Healthy signals 50

4.6 Accuracy 55

5. CONCLUSIONS AND RECOMMENDATIONS 57

5.1 Conclusions and Recommendations 57

REFERENCES 58

vi

LIST OF FIGURES (Cont’l)

FIGURE TITLE PAGE

Figure 1 Shape of normal ECG, including a U wave 1

Figure 2 Cardiovascular Abnormality Diagnosis Process 5

Figure 3 Feature extraction technique 6

Figure 4 Block diagram of heart disease classification through ANFIS 7

Figure 5 Results for normal data 8

Figure 6 Results for abnormal data 8

Figure 7 ANN based classification of ECG 9

Figure 8 Graph showing performances of the classification 10

Figure 9 Proposed computer aided diagnostic system 10

Figure 10 The algorithm proposed in the paper 12

Figure 11 Accuracy comparison with and without GA 12

Figure 12 Accuracies of various data sets for different K values 13

Figure 13 Graphical representation of accuracies achieved by using k-NN 14

Classifiers

Figure 14 k=7 nearest neighbor accuracy comparison 15

Figure 15 Schematic Representation of intermediate steps for KNN algorithm 15

implementation

Figure 16 Results obtained at each step of algorithm for lead V6 of record 15

M01_036 of CSE database a. raw ECG, b. filtered ECG,

c. gradient of ECG curve, d. QRS locations

Figure 17 ECG for a patient suffering from cardiomyopathy 18

Figure 18 ECG of a patient suffering from Myocardial Infarction 19

Figure 19 ECG of a patient suffering from dysrhythmia 20

Figure 20 ECG signals of the patient suffering from LBBB 21

Figure 21 ECG signals of the patient suffering from RBBB 21

Figure 22 ECG signals of normal people 22

Figure 23 All the 15 ECG signals recorded for every patient, the same way 23

in the PTB diagnostic database

Figure 24 Steps for Pan Tompkins method 24

Figure 25 Decomposition of Signal into sub-bands 26

Figure 26 Knn classifier function declaration in MATLAB 28

Figure 27 Raw Extracted ECG signal of Cardiomyopathy 30

Figure 28 Pan Tompkins algorithm output for cardiomyopathy 31

Figure 29 a. filtered signal with QRS points, b. MVI signal with QRS points 32

c. Pulse train of the found QRS on ECG signal of cardiomyopathy

Figure 30 QRS extracted ECG signal for cardiomyopathy 33

Figure 31 a. 1st differentiation, b. 2nd differentiation, c. cumulative 34

differentiation of cardiomyopathy signal

Figure 32 Dialog box showing that ECG suffers from Cardiomyopathy 34

Figure 33 Raw Extracted ECG signal of myocardial infarction 35

Figure 34 Pan Tompkins algorithm output for myocardial infarction 36


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c. Pulse train of the found QRS on ECG signal of myocardial

Figure 36 QRS extracted ECG signal of myocardial infarction 38


differentiation of myocardial infarction

Figure 38 Dialog box showing that ECG suffers from myocardial 39

Figure 39 Raw Extracted ECG signal of dysrhythmia 40

Figure 40 Pan Tompkins algorithm output for dysrhythmia 41


c. Pulse train of the found QRS on ECG signal of dysrhythmia

Figure 42 QRS extracted ECG signal of dysrhythmia 43


differentiation of dysrhythmia

Figure 44 Dialog box showing that ECG suffers from dysrhythmia 44

Figure 45 Raw Extracted ECG signal of bundle branch block 45

Figure 46 Pan Tompkins algorithm output for bundle branch block 46


c. Pulse train of the found QRS on ECG signal of BBB

Figure 48 QRS extracted ECG signal of bundle branch block 48


differentiation of bundle branch block

Figure 50 Dialog box showing that ECG suffers from BBB 49

Figure 51 Raw Extracted ECG signal of healthy signals 50

Figure 52 Pan Tompkins algorithm output for healthy signals 51


c. Pulse train of the found QRS on ECG signal of healthy signals

Figure 54 QRS extracted ECG signal of healthy signals 53

Figure 55 a. 1st differentiation, b. 2nd differentiation, c) cumulative 54

differentiation of healthy signals

Figure 56 Dialog box showing that ECG is healthy 54

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LIST OF TABLES

TABLE TITLE PAGE

Table 1 Table showing feature ranking of normal and CAD 11

Table 2 Results of evaluating the KNN algorithm using CSE database 16

Table 3 Results of evaluating the KNN algorithm using MIT-BIH 16

Arrhythmia database

Table 4 Confusion matrix obtained when the value of k=1 55

Table 5 Confusion matrix obtained when the value of k=3 ` 55

Table 6 Confusion matrix obtained when the value of k=5 55

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LIST OF ABBREVATIONS

ECG Electrocardiogram

CAD Coronary Artery Disease

EST Exercise Stress Test

DWT Discrete Wavelet Transform

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CHAPTER 1

INTRODUCTION

1.1 Background

ECG stands for electrocardiogram. It is also abbreviated as EKG in some countries. The

things that can be expected from an ECG are: it provides an evidence to support diagnosis,

for managing any cardiac rhythms that are abnormal, it also helps in diagnosing patients with

chest pain, etc. Electrocardiogram is a procedure of medical diagnosing which records the

heart electrical activity and shows it as a waveform. ECG is also considered as a painless,

noninvasive way to help diagnosing many heart problems for people of all ages. However,

ECG is an important tool but not as an end. The electric potential measured by an

instrumentation amplifier at various points of the body connected via leads to the body that

are attached to electrodes is termed as Electrocardiogram (ECG). There are basically three

types of ECG. They are

3-lead electrocardiogram, serves the purpose in transport monitors, and it focuses mainly on

two regions of the heart (lateral and inferior).

5-lead electrocardiogram, mostly serves the purpose in an ICU, in this type it also monitors

on the third area (anterior)

12-lead electrocardiogram known for its detailed information at three areas of the heart (the

front part is the anterior, the side part is the lateral, the back part is the inferior).

The heart’s electrical picture is called as a lead.

Figure 1: Shape of normal ECG, including a U wave

The atria muscle mass is small when it is compared to that of ventricles. The electrical

change in the contraction of the atria is also considered as small. The ECG wave called

‘P’ is associated with the contraction of atria. The mass of ventricles is considered large, a

large deflection can be seen in the ECG when the ventricles are depolarized: it is called as

the ‘QRS’ complex. The ‘T’ wave of the ECG is associated with the return of the

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ventricular mass to its resting electrical state (‘repolarization’). In some ECGs, an extra

wave can be seen on the end of the T wave, and this is called a U wave.

The six limb leads (1, 2, 3, VR, VL, VF) look at the heart from the sides and the feet in a

vertical plane. The chest or V leads look at the heart from the front and the left side in a

horizontal plane. Lead V1 is positioned over the right ventricle, and lead V6 over the left

ventricle.

1.2 Statement of the problem

As per the data given by WHO many people die due to heart diseases and it is considered as

the second greatest killer. So, any upgradations or improvements in treatments or diagnosis

tools would be of great help to the society and are most encouraged in the medical field. For

the treatment of heart diseases, several diagnostic tools are available. The tests available for

diagnosing include EST (Exercise stress test), Tread mill test, Electrocardiogram (ECG),

Echocardiogram and cardiac catheterization. Cardiac catheterization considered as the most

invasive method and time consuming as well, a lot of care is to be taken while catheters are

placed in the patient arteries or veins. Cardiac catheterization is considered as the most

painful method and patients also suffer with irritation. In some diagnostic tests, such as EST

and treadmill, electrophysiological data is recorded that is overstressed and this data is

obtained using ECG. Sometimes these tests are so dangerous that they may cause cardiac

arrest. For detection of CAD, standard ECG is obtained using physiological systems. For

identifying particular cardiac disease type ECG signals with subtle changes are picked up. It

is difficult for identifying minute changes visually in ECG signals. Some diagnostic tools that

are computer aided can help in improving cardiac health diagnosis accuracy by detection of

those small variations that occur in electrocardiogram signals.

Here in this project we detect diseases from the ECG signals by digital signal processing

them. Once the diseases are detected then appropriate diagnosis will be given. So, this will be

of great help to the patients as well as doctors as everything is done by the system and just a

pre check by the doctor would be enough. There are various cardiovascular diseases that can

be detected and classified using ECG signals.

1.3 Objectives

The objectives of this thesis are:

Classification of electrocardiogram signals using machine learning.

Digital signal processing of the prerecorded ECG signals that are obtained from

physionet.org for diagnosing of cardiomyopathy, myocardial infarction, myocarditis,

dysrhythmia and healthy signals.

The detection of features is to be done so that these features are taken into reference

for the comparison with the training dataset.

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1.4 Scope and limitations

This research study will help in classifying 5-6 heart diseases and diagnosing them. We are

limiting the detection and classification for only limited number of diseases. In the future, this

processing can be done for detecting and classifying more number of various other cardiac

diseases and this classification can be a reference to the doctor. Only those 5-6 diseases

mentioned will be triggered correctly and any other disease will be classified to the disease

which has almost similar ECG signals. Any disease that is to be classified should be trained,

then only it can be classified otherwise it gets classified to the disease having similar ECG

signals of those that are already trained.

1.5 Research Outline

The outline of this thesis is

In chapter 2, literature review of some of previous related studies are provided in brief.

In chapter 3, the methodology of this thesis paper is provided.

In chapter 4, the implementation and results are provided for classification of diseases

In chapter 5, conclusion and recommendations are provided.

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CHAPTER 2

LITERATURE REVIEW

2.1 Review

“Most of the heart functioning is monitored using the ECG signals, since they show

how every part of the heart is functioning in a clear graphical manner. These ECG

signals are measured using different ways. Some of the types are 3 lead ECG, 5 lead

ECG and 12 lead ECG. Doctors on seeing these ECG signals say what is the defect in

the person’s heart and doctors get to know that by the variations in the P wave, QRS

complex, T wave. Usually in hospitals the ECG type that is mostly used is 12 lead

ECG where it gives information about all the three areas of the heart i.e anterior,

lateral and inferior. These ECG signals are normally printed on grids. Time is

represented by the horizontal axis and voltage is represented by the vertical axis. So,

ECG signals play a crucial role when it comes to heart checkup and it is better to

check ECG whenever a patient’s health is so serious. Diagnosing disease using ECG

signals is of much important application and that can save a lot of time for the doctors

if the information of disease is printed on the ECG sheet itself.”

2.2 Detection and Classification of heart diseases

There are various methods in which we can detect and classify ECG signals. In all

methods proposed so far detect or classify only one or two disease using various

algorithms. Mostly used methods for detecting are Pan Tompkins, Daubechies

Wavelet transform, etc, and for classification there are algorithms like k-Nearest

Neighbor (k-NN), Probabilistic Neural Network (PNN), Support Vector Machine

(SVM), ANN (Artificial Neural Network), Fuzzy Logic System and many other. ECG

signals are detected and classified using several methods are shown as follows:

2.2.1 Early prediction of cardiovascular diseases using ECG Signals

[1] The early stage is said to be when the patient is suffering from chest pain

and angina. For early prediction, the researcher used the database provided by

PhysioNet.org. From the database of PhysioNet.org the obtained signals are

preprocessed where the noise and artifact contamination is removed and for

this preprocessing IIR filter for example Chebyshev Type 1, Elliptic filter and

Butterworth filter are used. After the preprocessing is done then they went for

feature extraction. The main purpose of feature extraction is to select and

retain relevant information from the preprocessed signals. In this method for

the purpose of feature extraction the algorithm used is Pan Tompkins

algorithm as used in various research papers. Using this algorithm the QRS

complex is detected in the ECG signals. In this paper the classification of

abnormal CVDs that are caused due to the traditional risk factors such as

tobacco smoking are classified using the Support Vector Machine (SVM)

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technique. This paper would be of help to make faster the diagnosis

procedures since it is early prediction.

2.2.2 Classification of Cardiac vascular disease from ECG signals

[2] In this paper the abnormalities that are found in ECG signals are used for

identifying diseases like arrhythmia, bradycardia, tachycardia and ischemia.

These diseases are classified using the Adaptive Neuro Fuzzy Interference

System (ANFIS). For the feature extraction, they used Daubechies Wavelet

Transform and for the purpose of compressing the algorithm used is Huffman

coding, the reason for compressing to be used here is for sending this

diagnosed signal and the results to the nearby health center.

In this paper, they followed three steps: In the first step, they identify and

annotate the ECG signal for normal, bradycardia, tachycardia and ischemia

characteristic. In the second step, they presented the strategy for extracting the

features of each sample of heart disease using the Discrete Wavelet

Transform. In the last step, they went through the classification using

Adaptive Neural Fuzzy Interference System modeling.

The methodology or the process used in this paper is shown in the following

schematic:

Figure 2: Cardiovascular Abnormality Diagnosis Process

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The DWT implementation goes through various steps and they are shown as

below:

Figure 3: Feature extraction technique

The procedure of feature extraction in this paper follows some steps and they

are:

The wavelet type is chosen

The wavelet name is chosen

Number of levels required for decomposition are chosen.

As they mentioned in the paper, for feature extraction the DWT wavelet types

have been chosen. The ECG signals are decomposed using single-level one-

dimensional wavelet decomposition into time-frequency representations. The

wavelet names are chosen from Daubechies wavelet filters db4 and 5

decomposition levels have been chosen.

The decision making for classifying the signals, it is done in 2 stages: for

selection of coefficients they used DWT and ANFIS classifiers. The 4 types of

electrocardiogram signals which are taken from PhysioNet.org are classified

by ANFIS classifiers. In the Fuzzy Logic Toolbox, ANFIS is performed for

classification. The combination of back propagation gradient decent method

and least squares method together were used for the training of ANFIS. The

block diagram of features processed in ANFIS is shown below:

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Figure 4: Block diagram of heart disease classification through ANFIS

In this paper, they concluded saying that by the usage of DWT and ANFIS, the

diseases arrhythmia, tachycardia and bradycardia are classified. By the

simulation results they showed that DWT and ANFIS predicts the class of

heart disease so perfectly.

2.2.3 Removal of noise from ECG signals using digital Signal Processing

[3] In this paper for the removal of noise they used Pan and Tompkins

algorithm and they diagnosed diseases which by detecting changes in the R

peaks. The diseases that can be detected based on the R-R interval are

something like PVC (Premature Ventricular Contraction). The database taken

in this paper is from PhysioNet.org. They downloaded using ECG exporter

program.

In this paper, they used excel sheet for copying data of the ECG signal. They

bought the data copied in the excel sheet into the program using the command

‘xlsread’. After that the command diff () is used for differentiating the signals.

The reason for differentiating ECG signals is to get rid of the base line drift

problem after doing this attenuation of low frequencies happen, slope

information is also obtained by using this command. Squaring is done, to

make all the values positive. The position and peak value are obtained in the

next step. To get maximum value the command max () is used. The heart beats

number is obtained for a time period which is given by the variable which

contains peak values and this is done by finding the length of that variable. R-

R interval is calculated in the following step. The difference of (n+1)th

position and nth value gives the number of data present between 2 R-R peaks.

In this paper, they concluded by saying that normal electrocardiogram signals

always have R-R interval in a specified range and R-R interval for signals

affected by something like PVC has time period more than specified range.

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The results in this paper are as shown below:

Figure 5: Results for normal data Figure 6: Results for abnormal data

2.2.4 ANN based Arrhythmia Diagnosis

[4] In this paper, they concentrated on classifying 2 types of arrhythmias

LBBB (Left bundle branch block) and Right bundle branch block (RBBB)

with healthy signal. As in all the research papers, this paper also first de noised

the ECG signal and the required features are detected from the pure signal.

The signal from which features are extracted is then given to the ANN

algorithm for classifying the two types of arrhythmia that are mentioned. For

this classification, electrocardiogram signals are taken from the

electrocardiogram MIT-BIH arrhythmias database.

In feature extraction, R peaks are detected initially. The samples taken from

the database are submitted to DWT which produces the coefficients of the

wave. Here the feature vector is created and is processed using Artificial

Neural Network (ANN) by error back propagation method for classifying

cardiac arrhythmia. “The patterns of the ECG signals are taken as neurons in

ANN. The neurons are organized in a feed forward network as a layered

structure and are connected strictly in a feed forward manner.

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Figure 7: ANN based classification of ECG

ANN is a network inspired biologically which is suitable in classifying

biomedical data. The number of parameters decides the precision of

classification results. The number of parameters that are selected for

classification equals the neurons number in the input layer. Training of ANN’s

is done by the parameters extracted. Typically for the classification, multilayer

feed forward network is used for configuration along with activation function

Log-sigmoid, for minimizing the squared error between the ANN’s actual

output and desired output the training is done using back propagation method.

Some important steps in BP algorithm are as follows:

Begin

Initiation of network

Learning sample is initialized

Computation of each neuron in network layer

Train error computing

Network weight is modified

Meet the error precision = 0.0001

End of process.

When the square errors sum for the segments is < or = .0001 then the training

of the ANN (Artificial Neural network), said to be completed. The vectors

taken as inputs of the ANNs classification are based on feature vectors

extracted using DWT from R wave. The success and error recognition of ANN

is determined by usage in testing phase.

The classification performances are expressed in terms of accuracy, precision,

sensitivity and specificity. The results are as follows:

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Figure 8: Graph showing performances of the classification

2.2.5 Diagnosis of CAD using nonlinear features extracted from ECG

signals

[5]In this paper, CADs are detected and classified from the normal signals.

Automated diagnosis of CAD is done using DWT and nonlinear feature

extraction techniques like: Tsallis entropy, renyi entropy and Multivariate

Multi-Scale Entropy (MMSE). Based on t-value the extracted features that are

obtained after DWT are ranked and fed to Support Vector Machine (SVM),

Decision Tree (DT), Probabilistic Neural Network (PNN) and K Nearest

Neighbor (KNN) for classification of CAD and normal classes.

In this paper the computer aided tool that is proposed includes acquisition of

signals, pre-processing, discrete wavelet transform, feature extraction, feature

selection, feature ranking and classification. The ECG signals that are

acquired, fed into de-noising technique which is wavelet based for baseline

removal. Using Pan Tompkins algorithm, the R peak detection is carried out.

Using detected R peaks the ECG signals are segmented. The ECG beats

acquired are subjected to DWT and are decomposed up to 4 levels. From

DWT coefficients features such as Multivariate Multi-Scale Entropy (MMSE),

Tsallis entropy and Renyi entropy are extracted. Using t-value the features are

ranked. The features that are ranked are then fed to classifiers such as k-

Nearest Neighborhood (KNN), Probabilistic Neural Network (PNN), Support

Vector Machine (SVM) and Decision Tree (DT) for determining the best

performing classifier. The following figure shows the computer aided tool for

diagnosis:

Figure 9: Proposed computer aided diagnostic system

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In the preprocessing stage the noise is removed from the ECG signals using

wavelet based denoising method. R-peak detection is done using Pan

Tompkins algorithm. This algorithm undergoes differentiation, squaring and

moving integrator. For beat segmentation the R point is considered as a

reference point. For feature extraction, various techniques are used as

mentioned. The technique MMSE is a univariate technique that regulates

intrinsic correlations that are nonstationary in the ECG signals and the amount

of correlation in the time series signal is improved. The physical behavior of a

system is described by Tsallis Entropy. It characterizes systems with long time

interactions, long range memory outcomes and multi fractal space time

constraints. The general form of Shannon entropy is Renyi entropy. The Renyi

entropy is used since Shannon entropy failed to illustrate the sudden changes

in the time series data. Higher entropy has flat and broad probability

distribution. Low entropy has narrow and peak distribution. Based on feature

ranking methods significant features along with necessary information are

selected. Based on their t-value, the features are ranked. For classifying

normal and CAD subjects using ECG signals the above-mentioned classifiers

are used. The results mentioning about the ranking of features for normal and

CAD are as shown below:

Table 1: Table showing feature ranking of normal and CAD

2.2.6 Classification of Heart diseases using k-NN and genetic algorithm

[15] In this paper, they used data mining which is extracting knowledgeable

information from large amount of data. They classified heart diseases using k-

NN classification and genetic algorithm. Their approach of combining k-NN

and genetic algorithm helped in improving classification accuracy of heart

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disease data set. The classification algorithm is built taking evaluated

attributed into consideration. Their classifier is trained to shoe the output

whether the person is sick or healthy. The algorithm used is divided into two

parts.

In the first part, genetic search is used for evaluating attributes.

In the second part, classifier is built and accuracy of the classifier is

measured.

Figure 10: The algorithm proposed in the paper

This approach has been tested with 6 medical data sets and 1 non-medical data

set. These data sets were taken from various corporate hospitals in Andhra

Pradesh, India. It is mentioned in the paper that, as k value is increasing the

accuracy is getting decreased. The attributes used in the paper are weather,

hypothyroid, breast cancer, liver disorder, primary tumor, heart stalog and

lymph. The results obtained by using k-NN and GA (genetic algorithm)

showing the accuracy are as follows:

Figure 11: Accuracy comparison with and without GA

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Figure 12: Accuracies of various data sets for different K values

In this paper, they finally conclude by saying that the accuracy is improved by

5% for heart disease in A.P. By their approach the accuracy of weather data

set is also increased by 14.29% From all the results, they conclude that the

accuracy is best obtained when integrating GA with KNN.

Comparison of this system with our proposed system:

Previous system (KNN with GA) Our system

1. In this system, for the classification

of heart diseases they used data sets

which consists of attributes like liver

disorder, primary tumor, breast

cancer, etc.

1. In our system, for the classification

of heart diseases we used ECG

signals.

2. In this system, the test data classifies

into healthy or sick.

2. In our system, we classify the test

data into various heart diseases

namely cardiomyopathy, myocardial

infarction, dysrhythmia, etc.

3. In this system, they used genetic

algorithm with k-NN classification.

3. In our system, we used DWT with k-

NN classification.

2.2.7 Medical Decision Support System for Diagnosis of Cardiovascular

Diseases using DWT and k-NN

[14]In this paper, signals are detected using DWT and classified using k-NN

classifier. The main of their study is to classify normal ECG signals from left

bundle branch block, right bundle branch block, atrial premature contractions

and premature ventricular contractions. The methodology used here is, in the

first step, ECG signal is decomposed into low frequency bands and used in

scale-2. In the second step the features are extracted. In the third step testing is

on an unknown ECG signal and classified into normal or diseased heart beat

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using k-NN classifier. They used three different k-NN classifiers and

compared the results obtained in three different classifiers. For this

classification, the database is obtained from MIT-BIH arrhythmia database

which is the most common database used in many previous researches. They

used 48 records for the total classification. They focused mostly on the k-NN

classifiers than on disease detection. The different k-NN classifiers used in this

paper are lbk, Kstar and LVL. The results show that LVL is able to get the

best accuracy out of the three.

Figure 13: Graphical representation of accuracies achieved by using k-NN Classifiers

2.2.8 Diagnosing of heart disease patients by applying kNN

[13] In this paper, focus is on investigating the integration of voting with kNN

classifier to diagnose heart disease patients and improve the accuracy. They

make this integration of k-NN and voting, apply it on benchmark dataset so as

to compare the accuracy obtained by using other techniques that are used on

same data set. Voting is a technique that combines the decisions from multiple

classifiers. The training data divides into equal, smaller subsets and one is

built for each subset of data. The majority voting is considered the simplest

form of voting where each classifier contributes a single vote. The class

getting the maximum votes is considered as the final prediction. The data used

in this paper is the benchmark Cleveland Clinic Foundation Heart disease data

set. It has 76 raw attributes. Applying voting to k-NN did not increase any

accuracy but applying it to the decision tree increased accuracy.

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Figure 14: k=7 nearest neighbor accuracy comparison

2.2.9 QRS Detection using K-Nearest Neighbor algorithm (KNN) and

Evaluation of standard ECG database

[12] In this paper, K-NN classifier is used and QRS complex is detected in

signals. This algorithm is tested on 2 databases namely MIT-BIH arrhythmia

and CSE. To reduce the false detection and interference of noise, they used a

digital band pass filter. For QRS detection, they used gradient of the ECG

signal. In this a comparison is made between the accuracies obtained on the

proposed algorithm and previous research works. The algorithm used in this

paper and results obtained are as shown below:

Figure 15: Schematic Representation of intermediate steps for KNN algorithm

implementation

Figure 16: Results obtained at each step of algorithm for lead V6 of record M01_036 of

CSE database a. raw ECG, b. filtered ECG, c. gradient of ECG curve, d. QRS locations

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In this paper, they mainly focused on detection of QRS complex than on

diagnosing diseases. The detection in this paper is like if the algorithm

correctly detects QRS complex then it is true positive (TP), if the algorithm

fails to detect then it is said to be false negative (FN), if the algorithm detects

QRS complex even there is no QRS complex then it is false positive (FP).

Detection rate is given the equation as follows

Table 2: Results of evaluating the KNN algorithm using CSE database

Table 3: Results of evaluating the KNN algorithm using MIT-BIH Arrhythmia database

17

The present paper shows much research on algorithmic consideration, some

methodological aspects related to classification, the capabilities of detecting

QRS complexes.

2.2.10 Performance analysis of ANN for cardiac arrhythmia detection

[11] The main goal of researcher in this paper is to evaluate the performance

of various classifiers. The classifiers whose performance is evaluated are k-

NN, Naïve Bayes Classifier (NBC), Multi-Class Support Vector Machine

(MSVM). In this paper, Pan Tompkins algorithm is applied to the raw ECG

signal for the detection of QRS complex and the features extraction is done.

After the feature extraction, the signal is classified using three classifiers as

mentioned and is then classified as normal or abnormal.

Comparison:

2.3 Diseases Diagnosed

2.3.1 Cardiomyopathy

This disease totally refers to that related to heart muscle. When this disease

occurs, it shows many signs and symptoms. In this, we could see an enlarged,

thick heart muscle. In some cases, the heart’s muscle tissue should be replaced by

a scar tissue. The ventricles are enlarged and weaken in dilated cardiomyopathy.

Previous System (Performance

analysis system)

Our system

In this system, the focus is to

evaluate the performance of

various classifiers.

In our system, the focus is to

classify the ECG signal into

various diseases.

In this system, the output is

shown as normal or abnormal

In our system, the output

shown is the disease that ECG

holds.

They used only Pan

Tompkins and classified.

In our system, we even used

DWT for wave

decomposition.

They used MIT-BIH

arrhythmia database.

In our system, PTB diagnostic

database is used.

They only used 20 ECG

records for collecting various

beats.

In our system, more 100

records are used for collecting

various beats.

18

In the worse stage of cardiomyopathy, the heart becomes so weak. It's ability

to pump the blood decreases through the body and maintains a normal

electrical rhythm with difficulty. This may sometimes lead to failure of

heart or some arrhythmia where the heartbeats are irregular. This failure of

heart may also lead to build up fluid in some major parts of the body namely

lungs, abdomen, etc,. The also shows heart valve problems because of

weakening of heart.

Cardiomyopathy is of two types and they are acquired and inherited. For those

who are not born with this but they develop due to other diseases or conditions

are said to be "Acquired". For those who are passed the gene from the parents

are said to be "Inherited". Most of the times, what caused cardiomyopathy is

not known. This disease shows result on people of different ages. However,

different age groups show different types of cardiomyopathy.

Figure 17: ECG for a patient suffering from cardiomyopathy

2.3.2 Myocardial Infarction

Like in any muscle in humans, the heart needs to be supplied with oxygen and

nutrients. The oxygenated blood gets delivered to the heart muscle by the

branching arteries. If any one of the coronary arteries or branches get blocked,

then that portion of the heart starves of oxygen and this condition is called

“cardiac ischemia.”

In this type of cardiac ischemia which shows up for a long time, that portion of

heart tissue which is starving of oxygen dies. It is generally called as heart

attack, which is known by the name myocardial infarction” – literally, “death

of heart muscle.”

There are number of tests which help in the diagnosis of myocardial infarction

and some of those tests include blood tests, electrocardiograms (ECGs) and

coronary angiography. An ECG which is considered as the recording of

electrical activity of heart, if ST elevation is present then it may confirm an ST

elevation Myocardial Infarction (STEMI).

https://www.nhlbi.nih.gov/health/health-topics/topics/hf/

https://www.nhlbi.nih.gov/health/health-topics/topics/hvd/

19

Treatment of MI is time critical. For a suspected MI, the most immediate and

appropriate treatment is Aspirin. In those with low oxygen levels or shortness

of breath, Supplemental oxygen should be used. Chest pain is the most

common symptom seen in acute myocardial infarction.

Figure 18: ECG of a patient suffering from Myocardial Infarction

2.3.3 Dysrhythmia

An abnormal heart beat is considered as a cardiac dysrhythmia. In this the

rhythm is irregular in its pacing or there may be low or high heart rate. Some

type of dysrhythmias is considered as potentially life threatening where as

some are considered normal (such as sinus arrhythmia).

There are different types of dysrhythmias and these are classified based on

origin (atria and ventricles) and heart rate.

If the heart rhythm is more than normal heart rate (over 100 beats per

minute) is considered as tachycardia. Tachycardia can originate either

from atria or ventricles. Those that originate from atria are called

supraventricular dysrhythmias and these include

a. Atrial fibrillation

b. Atrial flutter

c. Wolff-Parkinson-White Syndrome

d. Paroxysmal Supraventricular tachycardia

Those that begin from ventricles are called as Ventricular

dysrhythmias and these include

a. Premature ventricular contractions

b. Ventricular fibrillation

c. Long QT syndrome

If the heart rhythm is less than normal heart beat (under 60 beats per

minute) is considered as bradycardia.

20

For testing of dysrhythmia there are various methods like holter monitor,

ECG, stress tests, event monitors, cardiac catheterization, etc.

Figure 19: ECG of a patient suffering from dysrhythmia

2.3.4 Bundle branch block

Bundle branch block is a condition in which there’s a delay or obstruction

along the pathway that electrical impulses travel to make heartbeat. The

blockage or delay may occur on the pathway that sends electrical impulses to

the left or right side of the bottom chambers (ventricles) of the heart. The

bundle branch block makes it harder for the heart to pump blood through the

circulatory system. There is no complete treatment for bundle branch alone but

any health condition which caused bundle branch block is need to be treated. It

doesn’t cause any symptoms in most of the people and they don’t even know

that they are having bundle branch block. In some people signs and symptoms

such as fainting and feeling as if they are going faint is seen.

There are two types of bundle branch block. One is RBBB (Right Bundle

Branch Block) and the other one is LBBB (Left Bundle Branch Block). Any

problem in the right side of the heart is indicated by RBBB, when the QRS

complex are with normal duration in a RBBB then they are considered healthy

only. LBBB is always considered to be an indication of heart disease, mostly

of left ventricle. Whenever RBBB is detected we need to first think about

atrial septal defect. Whenever LBBB is detected we need to think about

ischaemic disease and aortic stenosis. No action is required if the patient is

asymptomatic. LBBB indicate acute MI, if the patient was suffering from

chest pain.

21

Figure 20: ECG signals of the patient suffering from LBBB

Figure 21: ECG signals of the patient suffering from RBBB

2.3.5 Healthy ECG signals

The ECG showing no abnormalities are considered as healthy. Some of the

physical features of these healthy signals are as follows:

P wave: In leads I, aVF and V3- V6 it is completely upright. The duration of

this wave less than or equal to 0.11 sec. It should not be peaked or notched and

must generally be smooth.

PR interval: The duration of this wave should be between 012 and 0.20 sec.

For QRS complex: The time period is < 0.12 sec, amplitude should be > .5mV

in atleast a lead which is standard, at least one precordial lead should have

amplitude greater than 1.0mV. In general, there should be proceeding from V1

to V6, the R waves become taller whereas the S waves become smaller. At V3

or V4, the waves S and R become equal. This is also called as transitional

zone.

22

In ST segment: In a normal ECG, this should be isoelectric and slanting

upwards to the T wave. It is not depressed > 0.5mm in any lead.

For T wave: Its deflection should also be in the same direction of the QRS

complex for at least 5 of the 6 limb leads. In leads V2-V6, this should be

upright and inverted in aVR. In leads V3 and V4, there should be an amplitude

of at least 0.2mV and in leads V5 and V6 there should be at least 0.1mV.

QT interval: This interval should be less than 0.40 secs for males and for

females it should be 0.44 secs.

Figure 22: ECG signals of normal people

23

CHAPTER 3

METHODOLOGY

3.1 Databases

As we are using the pre-recorded ECG signals in this paper, we are able to get them from

PHYSIONET.ORG. PhysioNet website is a site that is completely dedicated to medical data

of different diseases. In this paper, we are mainly focusing on diagnosis of heart diseases that

to on diseases which can be detected using ECG signals. So for this purpose we chose “The

PTB (Physikalisch- Technische Bundesanstalt) Diagnostic ECG database” from the National

Metrology Institute of Germany, which has provided to all the users the compilation of

digitized ECGs for the purpose of research, algorithmic benchmarking or teaching. This

database contains 549 records from 290 patients (aged from 17 to 87). Each record includes

15 simultaneously measured signals: the 12 leads (I, II, III, aVR, aVL, aVF, v1, v2, v3, v4, v5, v6)

together with three frank lead ECGs (vx, vy, vz). In this database, they also provide us with

header file which contains the complete detailed clinical summary which include age, gender,

diagnosis, ventriculography, echocardiography and hemodynamics.

Figure 23: All the 15 ECG signals recorded for every patient, the same way

in the PTB diagnostic database

24

3.2 Pre-processing

Pre-processing is an important part to be done for the ECG signals obtained from the website

PhysioNet.org. To remove the artifact contamination and noise present in the ECG signals,

preprocessing is done which otherwise would affect the clinical information used for

interpretation. For ECG preprocessing many researchers used different types of filters. These

filters remove the unwanted signals and get the signal that is needed for the extraction of

desired features. Since we took the signals from an open database, it may contain noise,

powerline interference and artifacts. Therefore, it is necessary for preprocessing the signals

and for this some basic filters are used for filtering of noise and artifact.

3.3 Features Extraction

For retaining and selecting information from electrocardiogram signal, the feature extraction

serves the purpose. There are different ways in which this feature extraction can be done.

3.3.1 Pan Tompkins algorithm

In this paper, QRS complex is detected using Pan Tompkins algorithm (8). This algorithm

helps in detection of QRS by the analyzing amplitude, slope, etc, of the electrocardiogram

signal. For the detection of QRS complex using Pan Tompkins algorithm (8), the signal needs

to be passed through a filter, differentiator, squaring operator and integration phase. It is

passed into integration phase just before the thresholds are set and finally the detection of

QRS complex is done. The reason for passing through bandpass filter is to decrease the any

kind of disturbances in the signal namely muscle noise, power line interference and baseline

wander. The following steps are followed in Pan Tompkins algorithm:

Figure 24: Steps for Pan Tompkins method

25

Band pass Filtering

The band pass filter for the QRS detection algorithm reduces noise in the ECG signal by

matching the spectrum of the average QRS complex. This attenuates noise due to muscle

noise, power line interference, baseline wander, T wave interference. The pass band that

maximizes the QRS energy is in the 5Hz-35Hz range. The filter implemented in this

algorithm is composed of cascaded high pass and low pass Butterworth IIR filters.

Differentiator

The next processing step is differentiation, standard technique for finding the high slopes that

normally distinguish the QRS complexes from other ECG waves. The derivative procedure

suppresses the low frequency components of P and T waves, and provides a large gain to the

high-frequency components arising from the high slopes of the QRS Complex.

Derivative filter is given by the following equation:

(1)

Squaring Operation

The squaring operation makes the result positive and emphasizes large differences resulting

from QRS complexes; the small differences arising from P and T waves are suppressed. The

high frequency components in the signal related to the QRS complex are further enhanced.

This is a nonlinear transformation that consists of point by point squaring of the signal

samples.

Squaring operation is given by the following equation:

(2)

Moving Window Integration

The squared waveform passes through a moving window integrator. This integrator sums the

area under the squared waveform over a suitable interval, advances one sample interval, and

integrates the new predefined interval window. The half-width of window has been chosen as

27 to include the time duration of extended abnormal QRS complexes, but short enough that

it does not overlap both a QRS complex and a T-wave. MA (moving average) filter extracts

features in addition to the slope of the R wave.

Moving average is given by

(3)

Pan Tompkins algorithm is used in this paper by the function:

[qrs_amp_raw, qrs_i_raw, delay] = pan_tompkin(ecg,fs,gr)

For this function above the inputs are:

ecg: it is the raw ecg vector signal

fs: sampling frequency

gr: this is the flag to plot or not plot (to have a plot, it is set to 1 otherwise to 0)

26

Outputs are

qrs_amp_raw: gives the R waves amplitudes

qrs_i_raw: index of R waves

delay: due to filtering the signal is delayed by number of samples

3.3.2 Discrete Wavelet Transform

In this as we are using Discrete Wavelet Transform for extraction of features. Wavelet

concept is used as it helps in analyzing data more accurately. Wavelets exist for a finite

duration. Wavelets come in different sizes and shapes. They are chosen based on the

application. Normally, for analysis of non-stationary signal component, wavelet transform is

used as a tool. It helps in the decomposition of signals into various sub-bands and at varying

resolutions. The band limited signals which are used here are decomposed into approximation

(A1, A2, A3, A4, A5, A6, A7, A8) and detail (D1, D2, D3, D4, D5, D6, D7, D8) by using a

8-level wavelet decomposition and a Daubechies (db6) wavelet. The decomposition is done

as shown in the figure below. For extraction of information from ECG signals we used

wavedec() function in MATLAB which is for Multilevel 1-D wavelet decomposition. This

multilevel is used for extracting information from large data or images, etc.

Figure 25: Decomposition of Signal into sub-bands

For a signal x, DWT is calculated by passing the signal through a series of filters.

First they are passed through a low pass filter having impulse response g which results in

convolution of two:

(4)

This signal is also decomposed using a high-pass filter h, simultaneously. The outputs

obtained from the high pass filter are detail coefficients and from low pass filters,

27

approximation coefficients are obtained. Here, half of the samples are discarded since half the

frequencies of the signal have been removed per Nyquist’s rule. The filter output of low pass

filter g (A1) is then subsampled by 2 and further processed by passing it again through a new

low pass filter and a high pass filter with about half the cut-off frequencies of the previous

one, i.e.:

(5)

(6)

This wave decomposition halves the resolution as only half of each filter’s output is

characterized by the signal. Each output has only half the frequency band of the input, so now

the frequency resolution is doubled.

At each level in the fig 18, the signal gets decomposed into high and low frequencies.

Due to the decomposition process, the input signal should be a multiple of 2^n where n

denotes number of levels.

The wavelet decomposition in MATLAB is called by the function:

[C, L] = wavedec(X, N, ‘wname’)

The above equation returns decomposition of wavelet for a signal X at level N, using

‘wname’.

The ‘wname’ is chosen based on the application.

C is wavelet decomposition vector, L is the bookkeeping vector which contains the number

of coefficients by level.

3.4 Classification of ECG signals

3.4.1 Classification using k-NN

In this project for the purpose of classification of ECG signals we used k-nearest neighbors

algorithm (k-NN). In pattern recognition, the k-NN algorithm is a non-parametric method

used for classification and regression. In both cases, the input consists of the k closest

training examples in the feature space. The output depends on whether k-NN is used for

classification or regression. Here in our project since we are using k-NN for classification, so

the output is a class membership. An object is classified by a majority vote of its neighbors,

with the object being assigned to the class most common among its k nearest neighbors (k is

a positive integer, typically small). If k=1, then the object is simply assigned to the class of

that single nearest neighbor. The best choice of k depends upon the data; generally larger

values of k reduce the effect of noise on the classification, but makes boundaries between

classes less distinct.

The reason for using k-NN is that it is automatically non-linear, it can detect linear or non-

linear distributed data, tends to perform very well even with lot of data points. Since the data

in our project is more I went for k-NN as it is more effective if the training data is large.

28

The default behavior is to use majority rule. That is, a sample point is assigned to the class

from which the majority of the K nearest neighbors are from. When using the consensus

option, points where not all of the K nearest neighbors are from the same class are not

assigned to one of the classes. Instead the output CLASS for these points is NaN for

numerical groups, '' for string named groups or <undefined> for categorical groups. When

classifying to more than two groups or when using an even value for K, it might be necessary

to break a tie in the number of nearest neighbors. Options are 'random', which selects a random tiebreaker, and 'nearest', which uses the nearest neighbor among the tied groups to

break the tie. The default behaviour is majority rule, nearest tie-break.

The knn classifier in MATLAB is declared as shown below:

Fig 26: Knn classifier function declaration in MATLAB

Class = knnclassify(Sample, Training, Group)

Where the sample we used is the output that we obtained from the wave decomposition of

extracted raw ECG signal i.e test_feat.

3.5 Process of Execution

The above Pan Tompkins algorithm (8), Discrete Wavelet Transform, k-NN classification

and databases are used to get the output. We can detect diseases namely cardiomyopathy,

myocardial infarction, bundle branch block, dysrhythmia and healthy normal from the ECG

signals provided by the PhysioNet.org. To check whether there the signal is a diseased one or

not we need to follow the below two steps:

We need to train the database files to the system designed for classification and this

training should be repeated every time if any new file is updated to the database.

In the Outset of this we will see the creation of .mat files which will hold the

properties of the ECG data signals that help us in the stage of classification, and we

need not always carry the database signals, it will be enough if we have these .mat

files which are saved under the Dataset folder.

Under testing we need to select anyone signal that needs to be classified on type of

disease it has. After testing is done we get a dialog box showing whether the tested

signal has some disease or not.

29

3.6 Comparison

30

CHAPTER 4

RESULTS

Training is something same like learning. In this paper, we are training ECG signals, to

predict the test ECG signal based on all trained ECG signals.

After the training is done, we go for testing of ECG signals, when testing is done we get raw

extracted ECG signal, Pan Tompkins algorithm (8) output for each signal which shows raw

ECG signal, output of low pass filter, output of high pass filter, output obtained after filtering

with derivative filter, output of squaring function, output averaged with 30 samples length,

QRS on filtered signal, QRS on MVI signal, Pulse train of the found QRS on ECG signal,

QRS extracted ECG signal, first differentiation, second differentiation, cumulative

differentiation on ECG signal, Dialog box showing that ECG suffers from particular disease.

4.1 Testing of Cardiomyopathy

Figure 27: Raw Extracted ECG signal of Cardiomyopathy

31

Figure 28: Pan Tompkins algorithm output for cardiomyopathy

32

Figure 29: a. filtered signal with QRS points, b. MVI signal with QRS points, c. Pulse

train of the found QRS on ECG signal of cardiomyopathy

33

Figure 30: QRS extracted ECG signal for cardiomyopathy

34

Figure 31: a. 1st differentiation, b. 2nd differentiation, c. cumulative

differentiation of cardiomyopathy signal

Figure 32: Dialog box showing that ECG suffers from Cardiomyopathy

35

4.2 Testing of Myocardial Infarction

Figure 33: Raw Extracted ECG signal of myocardial infarction

36

Figure 34: Pan Tompkins algorithm output for myocardial infarction

37


train of the found QRS on ECG signal of myocardial

38

Figure 36: QRS extracted ECG signal of myocardial infarction

39

Figure 37: a. 1st differentiation, b. 2nd differentiation, c. cumulative differentiation of

myocardial infarction

Figure 38: Dialog box showing that ECG suffers from myocardial

40

4.3 Testing of Dysrhythmia

Figure 39: Raw Extracted ECG signal of dysrhythmia

41

Figure 40: Pan Tompkins algorithm output for dysrhythmia

42


train of the found QRS on ECG signal of dysrhythmia

43

Figure 42: QRS extracted ECG signal of dysrhythmia

44


differentiation of dysrhythmia

Figure 44: Dialog box showing that ECG suffers from dysrhythmia

45

4.4 Testing of Bundle branch block

Figure 45: Raw Extracted ECG signal of bundle branch block

46

Figure 46: Pan Tompkins algorithm output for bundle branch block

47


train of the found QRS on ECG signal of BBB

48

Figure 48: QRS extracted ECG signal of bundle branch block

49


differentiation of bundle branch block

Figure 50: Dialog box showing that ECG suffers from BBB

50

4.5 Testing of Healthy signals

Figure 51: Raw Extracted ECG signal of healthy signals

51

Figure 52: Pan Tompkins algorithm output for healthy signals

52

Figure 53: a. filtered signal with QRS points, b. MVI signal with QRS points, c.

Pulse train of the found QRS on ECG signal of healthy signals

53

Figure 54: QRS extracted ECG signal of healthy signals

54


differentiation of healthy signals

Figure 56: Dialog box showing that ECG is healthy

55

4.6 Accuracy

(7)

(8)

(9)

Table 4: Confusion matrix obtained when the value of k=1

Accuracy obtained when k=1 is 71.947





56

For the above obtained data, the input is taken as follows:

A - Myocardial Infarction – 50 signals

B – Cardiomyopathy – 5 signals

C – Dysrhythmia – 4 signals

D – Bundle branch block – 4 signals

E – Healthy signals – 12 signals

57

CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusions and Recommendations

With the proposed system, we can classify 5 ECG signals into respective diseases.

The diseases we focused in this paper are cardiomyopathy, myocardial infarction,

bundle branch block, dysrhythmia and healthy signals. The reason for focusing only

on these 5 signals is that I could obtain the database of these five diseases itself. If

many types of diseases database are provided we could work on that and classify

those diseases as well. For obtaining more perfect results, more number of signals are

required so that each disease can be trained more effectively.

Only those diseases mentioned in this paper can be classified and any other diseased

signal is tested the algorithm would provide nearest result. That is one limitation in

this paper.

One biggest recommendation is that the results are obtained with best accuracy when

there are many signals which can be used for the training purpose.

So, finally I want to conclude the paper by bringing into readers view that all the

objectives mentioned in this paper are achieved successfully.

58

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