(ecg) signals based on fractal features and svm technique.

84 Pages·2014·2.11 MB·English

ENHANCED AUTOMATIC IDENTIFICATION OF ARRHYTHMIA
IN ELECTROCARDIOGRAM (ECG) SIGNALS BASED ON
FRACTAL FEATURES AND SVM TECHNIQUE.
By
Maram Hasan Al-Alfi
Supervisor
Dr. Rashiq Marie
This Thesis Was Submitted in Partial Fulfillment of the Requirements
for the Master Degree in Computer Science
Faculty of Scientific Research and Graduate Studies
Zarqa University
Zarqa, Jordan
April, 2014

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ACKNOWLEDGMENTS

Thanks Allah , thanks so much because I would not have been able to complete this
thesis without His aid and support.
I would like to express my deepest appreciation to my advisor, Dr. Rashiq Marie for his
leadership, support,and attention to details.
I have enjoyed the aid and support of my mother who instilled in me confidence and a
drive for pursuing my MSc. degree.
Finally I would like to thank the staff members of the Department of Computer
Science at Zarqa University for their continuous aids.

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TABLE OF CONTENTS

Contents

List of Tables ................................................................................................................ v
List of Figures............................................................................................................... vi
List of Acronyms...........................................................................................................viii
List of Publications ........................................................................................................ix
Abstract in Arabic...........................................................................................................x
Abstract in English........................................................................................................xii
Chapter 1: Introduction ........................................................................................…...1
1.1 Overview ............................................................................................................1
1.2 Problem Definition..............................................................................................2
1.3 Thesis Contribution ............................................................................................2
1.4 Thesis Organization.............................................................................................3
Chapter 2: Literature Review and Related Work..................................................... 4

2.1 Introduction...................................................................................................... 4
2.2 Related Work.....................................................................................................4
2.3 Taxonomy and research definition....................................................................6
Chapter 3: Electrocardiogram (ECG) Biosignals and Fractal Geometry...….........8
3.1 Overview .............................................................................................................8
3.2 Properties of ECG Signals..................................................................................11

3.3 Normal cardiac Electrocardiogram versus Abnormal .......................................13

3.4 Fractal features .................................................................................................18

3.5 Explanation of Fractal Geometry and Fractal Dimensions ……….………...20

3.5.1 Calculating Fractal Dimensions………………………..…………............22
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3.5.2 Examples of Deterministic Fractals ………………………..…….........24
3.5.3 Fractals and Fractal Geometry applications ………………..……..........26
3.6 Fractal Features Extraction From ECG Signals..............................................28

3.6.1 Time Domain Methods of Estimating FD.................................................28
Chapter 4: ECG Feature Extracting using (PSM) and Classification with

(SVM)………………………..………………………………………………….........32

4.1 Introduction ...............................................................................................................................32

4.2 Feature Extraction Using Power Spectrum Method(PSM)...................................,…32

4.2.1 PSM Methodology Algorithm............................................................................37

4.3 Arrhythmia Classification based on SVM.......................................................................38

4.3.1 Multiclass SVM……………....................................................................,..........41

4.3.2 Application of OAA SVM Using Fractal Features in ECG Arrhythmia

diagnosis……………………………………………………………....…………………………….…42

Chapter 5: Experimental Evaluation.........................................................................................44

5.1 Dataset Description...............................................................................................................44

5.2 Experimental Results………………............................................................................ 47

5.2.1 Fractal features Extraction..................................................................................... 47
5.2.2 Classification with SVM........................................................................................ 51

Chapter 6: Conclusion and Future Work……………………………………………............53

6.1 Conclusion……………………………………………….……………….……………….…53

6.2 Future work………………….……………..……………………………………...........……54

References……………………………………………………………………………….…...…..….....55

Appendices……………………………………………………………………………..…..……..........59

Appendix A: Matlab Code ...................................................................................................................59

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IST OF ABLES

Table Title Page
Table 1 Description of the Used Dataset 45
Table 2 The Estimated FD Values for Normal Sinus Rhythm Signals 48
Table 3 The Estimated FD Values for Ventricular Premature 49
Arrhythmia Signals
Table 4 The Estimated FD Values for Atrial Premature Arrhythmia 49
Signals
Table 5 The Estimated FD Values for Right Bundle Branch Block 49
Arrhythmia Signals
Table 6 The Estimated FD Values for Left Bundle Branch Block 49
Arrhythmia Signals
Table 7 Distinct Range of FD Values for Sample ECG Signals Using 50
PSM
Table 8 Ranges of FD Values for Sample ECG Signal Using Katz’s 50
Method
Table 9 Ranges of FD Values for Sample ECG Signal Using 50
Higuchi’s Method
Table 10 Ranges of FD Values for Sample ECG Signal Using Hurst’s 50
Method
Table 11 Average of the Estimated FD Values 50

Table 12 Number of Training and Testing Beats Used 52
Table 13 Class Percentage Accuracy Achieved on the Testing PSM – FD 52
Values with a Total Number of 122 PSM - FD Training Values

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L F
IST OF IGURES
Figure Title Page
Figure 1 Propagation of the depolarization wave in the heart muscle 9
Figure 2 Typical shape of ECG signal and its essential waves 12
Figure 3 A Normal sinus rhythm 13
Figure 4 Premature Ventricular 14
Figure 5 Atrial Premature 15
Figure.6 Right bundle-branch block 16
Figure 7 Left bundle-branch block 17
Figure 8 Fern Leaf 19
Figure 9 Classical geometry objects 19
Figure 10 Fractal Curves 19
Figure 11 Demonstration of fractal dimensions with Euclidean line segments 20
Figure 12 Demonstration of fractal dimensions with Euclidean planes 22
Figure 13 The Koch Curve 24
Figure 14 The Sierpinski Triangle 25
Figure 15 (left) Normal Sinus Rhythm ECG signal of size
1024, (right) Zoom in version of Normal Sinus Rhythm
ECG signal of size 512 35
(left) Atrial Premature Arrhythmia ECG signal of
Figure 16 size 1024, (right) Zoom in version of Atrial Premature
Arrhythmia ECG signal of size 512 35
Measured power spectrum of Normal Sinus
Figure 17 Rhythm ECG signal (left-to-right and top-to-bottom) for
window size 1024; 512; 256; 128 36
Figure 18 Measured power spectrum of Atrial Premature 36
ECG signal (left-to-right and top-to-bottom) for window size -
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1024; 512; 256; 128
Figure 19 38
Estimate the Fourier Dimension q of ECG Signal
Figure 20 SVM Model 39

Figure 21 Hyper Plane 40
Figure 22 SVM method using fractal features for ECG Arrhythmia 43
diagnosis
Figure 23 (A),(B) ECG signals of Normal Rhythm 45
Figure 24 (A), (B) ECG signals of a Premature Ventricular Arrhythmia 45
Figure 25 (A),(B) ECG signals of a Atrial Premature Arrhythmia 46
Figure 26 (A),(B) ECG signals of Right Bundle-Branch Block Arrhythmia 46

Figure 27 (A),(B) ECG signals of Left Bundle-Branch Block Arrhythmia 46

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LIST OF ACRONYMS
AA Average Accuracy
AF Atrial Fibrillation
AFIB Atrial Fibrillation beat
AP Atrial premature beat
BII Heart Block
ECG Electro Cardio Gram
FD Fractal Dimension
FFT Fast Fourier Transform
HRV Heart Variability Beat
LBBB Left Bundle Branch Block
PDF probability distribution function
Poly Polynomial
PSDF Power Spectral Density Function
PSM Power Spectrum Method
PSM Power Spectrum Method
PVC Ventricular premature beat
RBBB Right Bundle Branch Block
RBF Radial Basis Function
RS Rescaled Range Method
RSF Random Scaling Fractal
SVM Support Vector Machine
SVT Supraventricular tachycardia

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