Biomedical Signal Extraction And Processing For Digital Time Database Generation And Abnormality Detection

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Abstract

In this research an expert system for automated detection of abnormality of electrocardiogram (ECG) signal is developed. For this purpose, an off-line data acquisition system from paper based ECG records is developed by using image processing techniques for the future development of an Indian standard ECG database. Binarization is done for conversion of TIFF formatted gray tone image to two-tone binary image with the help of histogram analysis, which almost removes the background noise (e g gridlines of ECG papers). The rest of the dotted noises are removed by runlength smearing technique. Thinning is also done to avoid the repetition of co-ordinate information in the dataset. The pixel-to-pixel co-ordinate information is extracted and calibrated using prior information and converted into ASCII data-file. The present database contains 100 normal and 100 diseased subjects. Out of the diseased 100 patients, 55 patients have Myocardial Infarction (MI) and the remaining patients have Myocardial Ischemia. This work is reported in the paper I of original paper list which is given in page (iv) of this thesis. The 2nd_{chapter is} prepared on the basis of this topic.

The developed ECG database is further processed for removal of six different types of noises that can corrupt the ECG signals. All the noises are simulated and removed by digital FIR filters with the help of a software package Cool Edit Pro offered by Syntrillium Software Corporation. This is done to get a realistic situation for the algorithm and also to get greater accuracy in time-plane features detection.

In the next step, different time-plane features of ECG signals, which are important for ECG interpretation and classification, are extracted. At first, the R-R intervals (QRS complex) are detected using square derivative curve of ECG signal. Base-line is also detected for accurate detection of P wave and ST segments. Standard assumptions are used for detection of baseline, T waves and ST segments region. P wave is detected from the first derivative of the samples. Depending on the zero-crossings and shape of the signal, a syntactic approach is used for detection of P QRS and T waves. The accuracy level for QRS was 99.4%, for T waves was 96.7% and for P waves 92.2%. The QRS or R-R interval detection part is reported in paper IV whereas the noise removal and the other features extraction methods are reported in VI and IX. The 3rd_{chapter of the thesis is prepared on the basis of these papers.}

Frequency plane analysis is also done for extracting frequency plane features, which plays an important role for heart disease categorization. Both amplitude and phase properties are achieved which are established into ‘if-then’ rule-base for disease classification. This part of the work is described in the 4th chapter of the thesis and is reported in the papers II, III, V and VIII.

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Preface

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Acknowledgement

I would like to express my gratitude to Dr Madhuchhanda Mitra of Department of Applied Physics, University of Calcutta for giving me the opportunity to do this work and for her proficient supervision and guidance during the course of study.

I am deeply grateful to Professor B B Chaudhuri of Computer Vision and Pattern Recognition (CVPR) Unit of Indian Statistical Institute for his patient supervision and advice on the research work. I am indebted to him for his help in writing the reports, and revising the language of the thesis.

The financial support granted by Council of Scientific and Industrial Research (CSIR), Government of India is gratefully acknowledged.

I wish to thank Dr Arup Dasbiswas, Associate Professor of Institute of Post Graduate Medical Education and Research (IPGMER), University of Calcutta [S S K M Hospital, Kolkata] and Dr Ajoy Sarkar of Peerless Hospital and B K Roy Research Center, Kolkata for their expert opinion and collaboration regarding the medical aspects of this work.

I would like to thank Dr. S.B. Bhattachariya of Department of Cardiology, S.S.K.M. Hospital(IPGMER), Dr. B. Hulder, Asst. Prof., Dept. of Cardiology, Burdwan Medical College Hospital, Dr. T. Goswami, Medical consultant, Dr. B. Bala, Dr. S. K. Roy and Dr. A. K. Das of Uttarpara General Hospital, Dr. S. Chatterjee of Barakpur General Hospital, Dr. Kanak Kumar Mitra, Asst. Prof. , Dept of Cardiology, R. G. Kar Medical College, Dr. Basujit Gangopadhyaya of Apollo Clinic, Dr. P. C. Mandal of Anandalok Groups of Hospital, Dr. D. Ghosh Roy, Dr. T. K. Saha, Dr. Kaushik Mitra and Dr. D. Bhattachariya for sharing their experience and opinion with us and also for their cooperation and help.

I also wish to thank Mr. U. Garain of CVPR Unit and Dr. A. Raychowdhuri of Jadavpur University for their help in different occasions.

I thank Goutam Dhar, a final year B. Tech. student for his help to develop the computer program of P and T wave detection as a part of his final year project

I thank all the other faculty and staff members of CVPR Unit, Indian Statistical Institute and Department of Applied Physics, University of Calcutta for help and cooperation extended to me during the course of my research.

Finally, I would like to thank all my family members specially my husband Mr. Saibal Mitra and my parents Mr. D. L. Sarkar and Mrs. Sipra Sarkar for their unstinted support and inspiration during these years of my studies.

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List of Original Papers

This thesis is based on the following papers, which are referred to in the abstract by their Roman

numerals. The author is responsible for handling the whole problem, developing the computer

programming for all the modules, doing the experiment, analyzing the data and writing the

manuscripts.

I.

Sucharita Mitra

, M Mitra , “An Automated Data Extraction System From 12 Lead

ECG Images”,

Computer Methods and Programs in Biomedicine, (Elsevier Science

publication

), vol. 71(1), May(2003), pp 33-38.

II.

S. Mitra

, M. Mitra, “Phase Response Properties of Normal and Diseased ECG

Signals using an Automated Data Acquisition System”, technical journal of

The Institution

of Engineers (India),

vol. 84, May(2003), pp 14-17.

III.

S. Mitra

, M. Mitra & B. B. Chaudhuri , “Generation of Digital Time Database from

Paper ECG Records and Fourier Transform Based Analysis for Disease Identification”

accepted for publication in

Computers in Biology and Medicine, Elsevier Science

publication.

IV.

S. Mitra

, M. Mitra, “Detection of QRS Complex of ECG Signals from

Square-Derivative Curve”, accepted for publication in the

AMSE(France)

journal (Advances in

Modeling).

V.

S. Mitra

, M. Mitra, S. B. Bhattachariya & B. B. Chaudhuri , “Generation of Digital

Time Database from Paper ECG Records and Application of Frequency Plane Analysis for

Disease Identification”, Proceedings-CD of International Congress on Biological and

Medical Engineering (ICBME), 4-7 December, 2002 Singapore.

VI.

S. Mitra

, M. Mitra & B. B. Chaudhuri, "Time-plane Feature Extraction from ECG

signals for Development of Disease Classifier", Proceedings of International Conference on

Communication, Devices and Intelligent Systems (CODIS 2004), 8-10 January, 2004,

Kolkata, INDIA, pp. 653-656.

VII.

S. Mitra

, M. Mitra, S. Chattopadhyay & B. B. Chaudhuri, “An Approach to a

Rough-set Decision System for Classification of Different Heart Diseases”, Proceedings of

International Conference on Modeling and Simulation, MS’2004, Lyon, France, 5-7 July,

2004.

VIII.

Sucharita Mitra

, M. Mitra & B. B. Chaudhuri, “Frequency-plane Analysis of Normal and

Pathological ECG Signals in Application of Disease Identification”, accepted in

Journal of

Medical Engineering and Technology.

IX.

Sucharita Mitra

, M. Mitra & B. B. Chaudhuri, “ECG Features Extraction for Analysis

with the Help of an Off-line Data Acquisition Package”, communicated and under review to

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X.

Sucharita Mitra,

M. Mitra & B. B. Chaudhuri, “A Rough Set Based Inference Engine

for ECG Classification” communicated and under review at

IEEE Transactions on

Instrumentation and Measurement.

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LIST OF FIGURES

Figure1.1 A Schematic Diagram of the Circulatory System 3

Figure 1.2 The Anatomical Structure of Human Heart 4

Figure 1.3 Layers of the Heart Muscle 4

Figure 1.4 Pacemaker potentials and action Potentials in the SA node 6

Figure 1.5 An action potential in a myocardial cell from the ventricles 6

Figure 1.6 An ECG Strip 8

Figure 1.7 The placement of the bipolar leads and the exploratory electrode for the unipolar 9

chest leads in an electrocardiogram (ECG); (RA = right arm, LA = Left arm, LL = left leg ) Figure 1.8 The conduction of electrical impulses in the heart, as indicated by the 10

electrocardiogram (ECG) The direction of the arrows in (e) indicates that depolarization of the ventricles occurs from the inside (endocardium) out (to the epicardium), whereas the arrows in (g) indicate that repolarization of the ventricles occurs in the opposite direction Figure 2.1 Block Schematic of the Developed Off-line Data Acquisition Package 31

Figure 2.2 Gray Level Histogram of An ECG Image 33

Figure 2.3 Runlength Smearing Algorithm 34

Figure2.4 8 neighbors of P1 35

Figure 2.5 Image of original ECG signal from chart record 36

Figure 2.6 Extracted ECG signal before thinning 36

Figure 2.7 Reproduced ECG signal from extracted database after thinning 36

Figure 2.8 Image of original ECG signal from chart record 37

Figure 2.9 Extracted ECG signal before thinning 37

Figure 2.10 Reproduced ECG signal from extracted database after thinning 37

Figure 2.11 Original ECG images from image database and reconstructed signals 37

from digital time database Figure 2.12 Graphical analysis of standard deviation (for Ordinate) 41

Figure 3.1 Few noisy recorded ECG data 49

Figure 3. 2 (a) ECG Signal with 0% Noise Level 51

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Figure 3.2(c) ECG signal corrupted by 20% white noise 51

Figure 3.2(d) ECG signal corrupted by 30% white noise 51

Figure 3.3(a) ECG signal corrupted by 10% power line oscillation 52

Figure 3.3(b) ECG signal corrupted by 20% power line oscillation 52

Figure 3.3(c) ECG signal corrupted by 30% power line oscillation 52

Figure 3.4 A screen shot of 50 Hz notch filter working on ECG signal 52

corrupted by 10% power line oscillation Figure 3.5(a) ECG signal corrupted by 10% base line shift 53

Figure 3.5(b) ECG signal corrupted by 20% base line shift 53

Figure 3.5(c) ECG signal corrupted by 30% base line shift 53

Figure 3.6(a) ECG signal corrupted by 10% abrupt baseline shift 53

Figure 3.6(b) ECG signal corrupted by 20% abrupt baseline shift 53

Figure 3.6(c) ECG signal corrupted by 30% abrupt baseline shift 53

Figure 3.7 ECG signal corrupted with 20% composite noise 54

Figure 3.8(a) Noise free ECG signal 54

Figure 3.8(b) Filtered output of ECG signal 54

Figure 3.9 A screen shot of 0 6 Hz high-pass filter working on ECG signal 54

corrupted by 20% base line shift Figure 3.10 QRS complex or R-R interval detection 56

Figure 3.11 Filtered output of ECG signal after removal of all simulated noises 57

Figure 3.12 Graphical representation of 2nd order derivative after removal of 57

all simulated noises Figure3.13 Graphical representation of square of 2nd order derivatives after 57

removal of all simulated noises Figure 3.14 Reproduced ECG signals with different shapes 58

Figure 3.15 Square Derivative Curve of reproduced ECG signals 58

Figure 3.16 Different time plane features which are extracted 59

Figure 4.1 Time-domain Representation of Sin-wave 68

Figure 4.2 Time-domain Representation of Square Wave 68

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Figure 4.4 Effect in the frequency domain of sampling in the time domain 70

(a) Spectrum of original signal (b) spectrum of sampling function (c) Spectrum of sampled signal with fs >2fc (d) Spectrum of sampling function with fs < 2fc (e) Spectrum of sampled signal with fs < 2fc Figure 4.5 Block Schematic of the Proposed System 73

Figure 4.6 Amplitude diagram of Infarction and Normal patients for lead V4 77

Figure 4.7 Amplitude diagram of Ischemia and Normal patients for lead V6 78

Figure 4.8 Phase diagram of H7 for lead AVL for (a) Infarction Patients, 82

(b) Normal subjects Figure 5.1 Block Schematic of the Proposed System 90

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LIST OF TABLES

Table 2.1 QRS Portion Of Extracted Database Of The Image Shown In Figure 2 5 39

Table 2.2 QRS Portion Of Extracted Database Of Another Image 40

Table 3.1 QRS Detection Accuracy In Different Noise Levels 61

Table 3.2 T Wave Detection Accuracy In Different Noise Levels 62

Table 3.3 P Wave Detection Accuracy In Different Noise Levels 62

Table 3.4 A Portion Of Extracted Time Plane Features 62

Table 4.1 A Portion Of Numerical Values Of Amplitude And Phase Of ECG After DFS For Lead V6 75

Table 4.2 A Portion Of Numerical Values Of Amplitude And Phase Of ECG After DFS For Lead V4 76

Table 4.3 A Portion Of List Of Sum Of First Five Harmonics 79

Table 4.4 A Portion Of List Of Sum Of First Two Harmonics 79

Table 4.5 Phase Properties Of Normal Vs Myocardial Infarction 80

Table 4.6 Phase Properties Of Normal Vs Myocardial Ischemia 80

Table 4.7 Result Obtaine From Rule Based Classifier 81

Table 5.1 A Potion Of Decision Table 93

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Biomedical Signal Extraction And Processing For Digital Time Database Generation And Abnormality Detection

Abstract

Preface

List of Original Papers

Sucharita Mitra

The Institution

publication.

Disease Identification”, Proceedings-CD of International Congress on Biological and

S. Mitra

Pathological ECG Signals in Application of Disease Identification”, accepted in

for ECG Classification” communicated and under review at

LIST OF TABLES

CONTENTS