DIAGNOSING HEART DISEASES USING ANN AND GA
GHASSAN NASHAT MOHAMMED
UNIVERSITI UTARA MALAYSIA
2009
DIAGNOSING HEART DISEASES USING ANN AND GA
A Thesis submitted to the Graduate School in partial fulfilment of the requirements for the degree
Master of Science (Information Technology) Universiti Utara Malaysia
By
Ghassan Nashat Mohammed
Copyright © Ghassan Nashat, 2009. All rights reserved.
PERMISSION TO USE
In presenting this thesis in partial fulfilment of the requirements for a postgraduate degree from University Utara Malaysia, I agree that the University Library may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by my supervisor in his absence, by the Dean of the Faculty of Information Technology. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain should not be allowed without my written permission. It is also understood that due recognition shall be given to me and to University Utara Malaysia for any scholarly use which may be made of any material from my thesis.
Requests for permission to copy or to make other use of materials in this thesis, in whole or in part, should be addressed to
Dean of Faculty of Information Technology Universiti Utara Malaysia
06010 UUM Sintok
Kedah Darul Aman
ABSTRACT
The heart is complex systems that reveals many clues about its condition in electrocardiogram (ECG), and is one of the most important organs in a human body .The walls of the heart contain myocardial tissues which contract to push the blood through the body. This contract occurs because of passing electrical current in the heart muscle the electrical current can be captured and analyzed to diagnose the heart state. This operation is done by using electrocardiograph (ECG) device; this device captures the electrical signal, filters it from noise signals, and amplifies it. Then it displays the signal on the screen or prints it on the trace paper then the doctor interprets the ECG signal to diagnose the disease.
This project discusses using artificial intelligent (AI) to process and analyze the ECG signal to diagnose the heart disease directly and display detailed report about the heart state by using the artificial neural network (ANN) after training it and finding the values of the connection weights using the genetic algorithm (GA) to choose the best values to the weights.
The GA is qualified in enhancing the weights of the ANN since the ANN is trained using the classical algorithm (back-propagation), the genetic algorithm is used as a co-training algorithm for enhancing the connection weights values and minimizing the error value.
ACKNOWLEDGEMENTS
I would like to offer my sincere thanks to my supervisor Dr.Azman Bin Yasin, for his excellent guidance and moral support, his time and for his patience and commitment in helping me complete this research.
I would also like to offer my deepest gratitude to my parents, my wife Mrs.Suha Saheb for their constant encouragement and moral support, which enabled me to successfully accomplish my tasks.
I am deeply indebted to my dear friends and colleagues for taking so much of interest in my work, always being there for me through my difficult situations and spending their time in guiding me despite their busy schedule.
Last but not least, thanks to all those who have been directly and indirectly involved in helping me completing this research.
TABLE OF CONTENTS
I ii iv v vi 1 1 2 4 5 5 6 6 6 6 7 7 8 8 8 8 8 9 10 11 13 14 14 15 16 16 17 17 18 18 19 19 20 24 25 25 26 27 27 27 28 29 29 ABSTRACT……….
ACKNOWLEDGEMENTS……….
LIST OF TABLES ...
LIST OF FIGURES……….
LIST OF ABBREVIATIONS……….
CHAPTER 1 ………...
INTRODUCTION ………..
1.1Definition ………
1. 2 Neural Network ……….
1.3 Genetic Algorithm ………...
1.4 Background Study ………..
1.5 Problem Statement ………. … 1.6 Research Question ……….. ..
1.6 Research Objective ………....
1.7 Significant Of Study ………..
1.8 Scope ……….
1.9 Thesis Overview ……….
CHAPTER 2………
LITERATURE REVIEW ………...
2.1 Introduction………
2.2 Neural Network………
2.2.1 Classical Algorithm For Training Neural Network………...
2.2.2 Back Propagation ………..
2.2.3 Application Of Neural Network In ECG Analysis………
2.3 Genetic Algorithm………..
2.3.1 Genetic Operator………..
2.3.1.1 Initialization…… ………
2.3.1.2 Fitness Function……… … 2.3.1.3 Selection Methods………..
2.3.1.4 Reproduction………..
2.3.1.5 Crossover………
2.3.1.6 Mutation……….
2.3.2 GA And ANN Back Propagation………..
2.3.3 Outline Of The Algorithm……….
2.4 Electrocardiogram (ECG)………
2.4.1 Signal Description……….
2.5 Classification………
2.5.1 Neural Network Studies……….
2.5.2 Fuzzy Logic Studies………..
2.5.3 Cardiac Electrical Signal Studies………..
2.5.4 Non Linear Technique Studies………
2.5.5 Wavelet Algorithm Studies………
CHAPTER 3………..
RESEARCH METHODOLOGY………
3.1 Introduction ………
3.2 Planning ………..
3.3 Requirement ………
3.3.1 Matlab ………
30 30 30 32 32 33 33 34 34 39 40 41 41 42 43 43 43 44 46 48 51 60 60 60 60 60 61 61 62 68 3.3.2 Computer, Windows XP. ………..
3.4 Design ……….
3.4.1 The structure Of ANN And Training It Using GA……….
3.4.2 Build GA Model ……….
3.4.3 Pseudo Code For GA ………
3.5 Creation: ……….
3.5.1 Create Initial Population ……….
3.5.2 Parameter Setting……….
3.5.3 Fitness Computation ………
3.5.3 Selection ………...
3.5.4 Crossover Operation: ………...
3.5.5 Mutation Operation………..
3.6 Testing ………..
3.7 Summary……….
CHAPTER 4………..
FINDINGS AND RESULT……….
4.1 Introduction ………...
4.2 Training Results Of R Peaks Detector Neural Network ………
4.3 Training Result Of P Wave Detector ANN ………
4.4 Enhancing R Peak, P Wave Detectors ANN Weights by Using Genetic Algorithm. ………..
4.5 Main Program ………
CHAPTER 5……….
CONCLUSIONS AND RECOMMENDATIONS………
5.1 Introduction ………..
5.2 Experimentation Results ……….. ……
5.3 Future Work ………..
5.4 Conclusion ………..
5.5 Summary ………...
REFERENCES………
Appendix Example Of Input Pattern………
LIST OF TABLES
Table 3.1: GA parameter………...……… 34 Table 3.2: Example. ………...42
LIST OF FIGURES
LIST OF ABBR EVIAT
IONS
GA
Genetic Algorith m
ANN Artificial
Neural Network
ECG Electroc
ardiogra m
BP Back-
Propagat ion
SDLC System
Develop ment Life Cycle
Figure 1.1 Figure 2.1
Figure 3.1 Figure 3.2 Figure 3.3 Figure 3.4 Figure 3.5 Figure 3.6 Figure 4.1 Figure 4.2 Figure 4.3 Figure 4.4 Figure 4.5 Figure 4.6 Figure 4.7 Figure 4.8 Figure 4.9 Figure 4.10 Figure 4.11 Figure 4.12 Figure 4.13 Figure 4.14 Figure 4.15 Figure 4.16
Basic ECG shape of the normal
Back propagation in a c Back propagation in a connectionist network having a hidden layer System Development Life Cycle Methodology The ECG signal [11].
ANN architecture of the detectors The chromosome format
Crossover operation Mutation operation
Training graph of R peak detector neural network Simulation results of R peak detector
Training graph of P wave detector neural network Response of P wave detector
Results of optimizing R peak detector weights Testing R peak detector ANN Training Using GA Result of optimizing P wave detector weights Testing P wave detector ANN Training Using GA Calling main program
Rhythm interpretation program R peak detector subroutine
Example 1 represents sinus bradicardia
Example 2 represents normal case of ECG signal Example 3 represents a trial tachycardia
Example 4 represents sinus tachycardia Example 5 represents sinus arrhythmia
3 10
27 28 31 32 40 41 45 46 47 48 49 50 50 51 52 53 54 55 56 57 58 59
CHAPTER 1 INTRODUCTION
The human heart is a complex system that reveals many clues about its condition in electrocardiogram (ECG) signal, an example of ECG is provided in Figure 1.1.
Trained physicians are able to recognize patterns in a patient's ECG signal and use them as the basis for diagnosis [1], for instance to diagnose heart ailments such as arrhythmia [2], ischemia [3, 4],or prediction of an impending heart attack [5].
Researchers have tried since the inception of computers to develop techniques and algorithms for automated processing of ECG signals for various medical applications [6, 7], whether as standalone applications or as a decision aid to physicians.
Early detection of ischemia is crucial because, in most cases, the effects of myocardial ischemia are completely reversible if detected early enough [8, 9, and 10].
However, patterns of an ECG signal are difficult to discern due to the multitude of characteristics that are embedded in the signal. As a result, researchers have focused on developing specific techniques to extract information from ECG’s for specific applications.
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