Suri / Spaan Advances in Cardiac Signal Processing

1. Auflage 2007
ISBN: 978-3-540-36675-1
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 468 Seiten

Various disciplines have been bene?ted by the advent of high-performance computing in achieving practical solutions to their problems and the area of health care is no exception to this. Signal processing and data mining tools have been developed to enhance the computational capabilities so as to help clinicians in diagnosis and treatment. The electrocardiogram (ECG) is a representative signal containing inf- mation about the condition of the heart. The shape and size of the P-QRS-T wave and the time intervals between various peaks contains useful infor- tion about the nature of disease a?icting the heart. However, the human - server cannot directly monitor these subtle details. Besides, since biosignals are highly subjective, the symptoms may appear at random in the timescale. The presence of cardiac abnormalities are generally re?ected in the shape of ECG waveform and heart rate. However, by the very nature of biosignals, this re?ection would be random in the timescale. That is, the diseases may not show up all the time, but would manifest at certain irregular (random) intervals during the day. Therefore the study of ECG pattern and heart rate variability has to be carried out over extended periods of time (i. e. , for 24 hours). Naturally the volume of the data to be handled is enormous and its study is tedious and time consuming. As a consequence, the possibility of the analyst missing (or misreading) vital information is high.

Autoren/Hrsg.

Suri, Jasjit

Spaan, J.A.E

Inhaltsverzeichnis

1;Preface;5
2;Contents;11
3;List of Contributors;20
4;1 The Electrocardiogram;22
4.1;1.1 Anatomy of the Heart;24
4.2;1.2 Electrical Conduction System;27
4.3;1.3 Arrhythmias;39
4.4;1.4 Miscellaneous Electrocardiogram Changes;56
4.5;1.5 Myocardial Ischemia, Injury and Infarction;69
4.6;References;74
5;2 Analysis of Electrocardiograms;75
5.1;2.1 Steps in ECG Analysis;75
5.2;2.2 Preprocessing of ECG;76
5.3;2.3 QRS Complex Detection;80
5.4;2.4 Detection of QRS Complex Onset and Offset;85
5.5;2.5 ST Segment Analyzer;86
5.6;2.6 Complexity Analysis of ECG Signal;87
5.7;2.7 Sample Results and Discussion;89
5.8;References;101
6;3 Prediction of Cardiac Signals Using Linear and Nonlinear Techniques;103
6.1;3.1 Data Acquisition and Preprocessing;106
6.2;3.2 Modeling Techniques;107
6.3;3.3 Results;114
6.4;3.4 Discussions;123
6.5;3.5 Conclusion;124
6.6;References;125
7;4 Visualization of Cardiac Health Using Electrocardiograms;128
7.1;4.1 Introduction;128
7.2;4.2 Preprocessing;130
7.3;4.3 Arrhythmia Detection;130
7.4;4.4 Data Handling and Display;131
7.5;4.5 Hierarchical Display;133
7.6;4.6 Sector Graph Display;134
7.7;4.7 Discussion;137
7.8;4.8 Conclusion;138
7.9;References;138
8;5 Heart Rate Variability;140
8.1;5.1 Physiological Phenomenon of HRV;140
8.2;5.2 Introduction;141
8.3;5.3 Methods;150
8.4;5.4 Frequency Domain Analysis;153
8.5;5.5 Nonlinear Methods of Analysis;159
8.6;5.6 Requirements for Non-Linear Analysis;170
8.7;5.7 Discussion;171
8.8;5.8 Conclusion;173
8.9;References;174
9;6 Data Fusion of Multimodal Cardiovascular Signals;185
9.1;6.1 Approaches for Fusion;186
9.2;6.2 Rule Based Approach;189
9.3;6.3 Introduction to Fuzzy Based Decision Making;190
9.4;6.4 Fuzzy Logic Approach;192
9.5;6.5 Patient States Diagnosis System Implementing Data Fusion;195
9.6;6.6 Results and Discussion;197
9.7;6.7 Conclusion;202
9.8;References;202
10;7 Classification of Cardiac Patient States Using Artificial Neural Networks;205
10.1;7.1 Neural Networks;208
10.2;7.2 Backpropagation Learning Algorithm;209
10.3;7.2 Self-Organizing Maps as Classifier;211
10.4;7.3 Radial Basis Function Networks as Classifiers;214
10.5;7.4 Overview of Cardiac Patients States Classification System;216
10.6;7.5 Effect of Number of Training Data on Network Performance;220
10.7;7.6 Network Performance Analysis;222
10.8;7.7 Conclusion;224
10.9;References;225
11;8 The Application of Autoregressive Modeling in Cardiac Arrhythmia Classification;227
11.1;8.1 Introduction;227
11.2;8.2 Methods;229
11.3;8.3 Results;233
11.4;8.4 Discussion;240
11.5;8.5 Conclusion;242
11.6;References;242
12;9 Classification of Cardiac Abnormalities Using Heart Rate Signals: A Comparative Study;245
12.1;9.1 Neural Network Classifier;247
12.2;9.2 Inputs to the Classifier;248
12.3;9.3 Surrogate Data;249
12.4;9.4 Fuzzy Classifier;249
12.5;9.5 Neuro-Fuzzy Classifier;252
12.6;9.6 Results;257
12.7;9.7 Discussion;258
12.8;9.8 Conclusion;259
12.9;References;260
13;10 Storage and Transmission of Cardiac Data with Medical Images;262
13.1;10.1 Concept of Interleaving;263
13.2;10.2 The Interleaving Process;264
13.3;10.3 Encryption of the Text File;264
13.4;10.4 Encryption of the Graphic File;265
13.5;10.5 Interleaving in DFT Domain;267
13.6;10.6 Interleaving in DCT;268
13.7;10.7 Interleaving in Wavelet Domain;268
13.8;10.8 Evaluation of Results;269
13.9;10.9 Interleaving Error Correcting Codes;272
13.10;10.10 Discussion;276
13.11;10.11 Conclusion;277
13.12;References;277
14;11 Assessment of Cardiac Function in Filling & Systolic Ejection Phases: A Mathematical and Clinical Evaluation;280
14.1;11.1 Introduction and Scope;280
14.2;11.2 Known Characterization of Left Ventricular Function;280
14.3;11.3 Justification for Clinical Indices of Cardiac Function;283
14.4;11.4 Passive and Active Elastances of the Left Ventricle;290
14.5;11.5 LV ds*/ dtmax as Noninvasive Contractility Index Based on Wall- Normalized Stress;296
14.6;11.6 Concluding Remarks;299
14.7;References;299
15;12 Arterial Wave Propagation and Reflection at a Bifurcation Site;305
15.1;12.1 Analysis for Pulse Wave Propagation Velocity;306
15.2;12.2 Depiction of Pulse Pressure Wave Propagation;312
15.3;12.3 Determination of Arterial Elasticity Parameters;313
15.4;12.4 Determination of Arterial Impedance from PWV and Arterial Cross- Section Area;316
15.5;12.5 Reflection at Arterial Bifurcation;321
15.6;12.6 Conclusing Remarks;324
15.7;References;324
16;13 ECG Signal Conditioning by Morphological Filters;327
16.1;13.1 Mathematical Morphology Operators;329
16.2;13.2 Proposed MMF Algorithm for ECG Signal Conditioning;330
16.3;13.3 Experimental Results and Discussion;333
16.4;13.4 Conclusion;340
16.5;References;341
17;14 Multivariate Analysis for Cardiovascular and Respiratory Signals;343
17.1;14.1 Method;345
17.2;14.2 Multichannel Spectral Analysis;346
17.3;14.3 Results and Discussion;347
17.4;14.4 Conclusion;350
17.5;References;351
18;15 Phase Space Analysis for Cardiovascular Signals;354
18.1;15.1 Method;356
18.2;15.2 Extension to Higher Dimension;358
18.3;15.3 Weighted Spatial Filling Index;358
18.4;15.4 Results and Discussion;359
18.5;15.5 Conclusion;365
18.6;References;365
19;16 Linear, Non-Linear and Wavelet Analysis of Cardiac Health Using Heart Rate Signals;370
19.1;16.1 Data Used for Analysis;372
19.2;16.2 Methods Used for Analysis;373
19.3;16.3 Results;374
19.4;16.4 Discussions;378
19.5;16.5 Surrogate Data;385
19.6;16.6 Conclusion;386
19.7;References;386
20;17 Soft Tissue Biomechanics of the Left Ventricular Myocardium;391
20.1;17.1 Introduction;391
20.2;17.2 Previous Research on Soft Tissue Biomechanics;392
20.3;17.3 Integrative Model;401
20.4;17.4 Conclusion;410
20.5;References;412
21;18 Wavelets and its Application in Cardiology;420
21.1;18.1 The Fourier Transform;420
21.2;18.2 Short Time Fourier Transform;421
21.3;18.3 Continuous Wavelet Transform (CWT);422
21.4;18.4 Discrete Wavelet Transform (DWT);423
21.5;18.5 Multi Resolution Analysis (MRA);424
21.6;18.6 Some Applications;427
21.7;18.7 Conclusion;434
21.8;References;434
22;19 1/f Fluctuation of Heart Rate in Postoperative and Brain- Dead Patients;436
22.1;19.1 Data Acquistion;437
22.2;19.2 Data Analysis;438
22.3;19.3 Results;439
22.4;19.4 Discussion;449
22.5;References;453
23;20 Stress During Speech Therapy;455
23.1;20.1 Speech Therapy;455
23.2;20.2 Heart Rate Variability and Stress;456
23.3;20.3 Calculating the RR Interval;456
23.4;20.4 Experimental Methods;457
23.5;20.5 Results;458
23.6;20.6 Program with Relaxation;458
23.7;20.7 Conclusion;461
23.8;References;461
24;Color Plates;462
25;Index;469