Librería Portfolio

The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over 350 figures and illustrations.

Wide range of filtering techniques presented to address various applications
800 mathematical expressions and equations
Practical questions, problems and laboratory exercises
Includes fractals and chaos theory with biomedical applications




Table of contents

Preface xvii

Acknowledgments xxii

Preface: First Edition xxiii

Acknowledgments: First Edition xxviii

About the Author xxxi

Symbols and Abbreviations xxxiii

1 Introduction to Biomedical Signals 1

1.1 The Nature of Biomedical Signals 1

1.2 Examples of Biomedical Signals 4

1.2.1 The action potential of a cardiac myocyte 4

1.2.2 The action potential of a neuron 11

1.2.3 The electroneurogram (ENG) 12

1.2.4 The electromyogram (EMG) 14

1.2.5 The electrocardiogram (ECG) 21

1.2.6 The electroencephalogram (EEG) 34

1.2.7 Event related potentials (ERPs) 40

1.2.8 The electrogastrogram (EGG) 41

1.2.9 The phonocardiogram (PCG) 42

1.2.10 The carotid pulse 46

1.2.11 Signals from catheter-tip sensors 48

1.2.12 The speech signal 48

1.2.13 The vibromyogram (VMG) 54

1.2.14 The vibroarthrogram (VAG) 54

1.2.15 Otoacoustic emission (OAE) signals 56

1.2.16 Bioacoustic signals 56

1.3 Objectives of Biomedical Signal Analysis 57

1.4 Difficulties in Biomedical Signal Analysis 61

1.5 Why Use CAD? 64

1.6 Remarks 66

1.7 Study Questions and Problems 66

1.8 Laboratory Exercises and Projects 69

2 Concurrent, Coupled, and Correlated Processes 71

2.1 Problem Statement 72

2.2 Illustration of the Problem with Case Studies 72

2.2.1 The ECG and the PCG 72

2.2.2 The PCG and the carotid pulse 73

2.2.3 The ECG and the atrial electrogram 74

2.2.4 Cardiorespiratory interaction 76

2.2.5 The importance of HRV 77

2.2.6 The EMG and VMG 78

2.2.7 The knee joint and muscle vibration signals 79

2.3 Application: Segmentation of the PCG 80

2.4 Application: Diagnosis and Monitoring of Sleep Apnea 81

2.4.1 Monitoring of sleep apnea by polysomnography 83

2.4.2 Home monitoring of sleep apnea 83

2.4.3 Multivariate and multi-organ analysis 84

2.5 Remarks 89

2.6 Study Questions and Problems 89

2.7 Laboratory Exercises and Projects 89

3 Filtering for Removal of Artifacts 91

3.1 Problem Statement 92

3.2 Random, Structured, and Physiological Noise 93

3.2.1 Random noise 93

3.2.2 Structured noise 100

3.2.3 Physiological interference 100

3.2.4 Stationary, nonstationary, and cyclostationary processes 101

3.3 Illustration of the Problem with Case Studies 104

3.3.1 Noise in event-related potentials 104

3.3.2 High frequency noise in the ECG 104

3.3.3 Motion artifact in the ECG 104

3.3.4 Powerline interference in ECG signals 104

3.3.5 Maternal interference in fetal ECG 106

3.3.6 Muscle contraction interference in VAG signals 107

3.3.7 Potential solutions to the problem 109

3.4 Fundamental Concepts of Filtering 110

3.4.1 Linear shift in variant filters 112

3.4.2 Transform domain analysis of signals and systems 124

3.4.3 The pole-zero plot 131

3.4.4 The discrete Fourier transform 133

3.4.5 Properties of the Fourier transform 139

3.5 Time domain Filters 143

3.5.1 Synchronized averaging 143

3.5.2 MA filters 147

3.5.3 Derivative based operators to remove low frequency artifacts 155

3.5.4 Various specifications of a filter 161

3.6 Frequency domain Filters 162

3.6.1 Removal of high frequency noise: Butterworth low pass filters 164

3.6.2 Removal of low frequency noise: Butterworth highpass filters 171

3.6.3 Removal of periodic artifacts: Notch and comb filters 173

3.7 Order-statistic filters 177

3.8 Optimal Filtering: The Wiener Filter 181

3.9 Adaptive Filters for Removal of Interference 196

3.9.1 The adaptive noise canceler 198

3.9.2 The least mean squares adaptive filter 201

3.9.3 The RLS adaptive filter 202

3.10 Selecting an Appropriate Filter 207

3.11 Application: Removal of Artifacts in ERP Signals 211

3.12 Application: Removal of Artifacts in the ECG 215

3.13 Application: Maternal-Fetal ECG 217

3.14 Application: Muscle contraction Interference 218

3.15 Remarks 220

3.16 Study Questions and Problems 222

3.17 Laboratory Exercises and Projects 230

4 Detection of Events 233

4.1 Problem Statement 233

4.2 Illustration of the Problem with Case Studies 234

4.2.1 The P, QRS, and T waves in the ECG 234

4.2.2 The first and second heart sounds 235

4.2.3 The dicrotic notch in the carotid pulse 236

4.2.4 EEG rhythms, waves, and transients 236

4.3 Detection of Events and Waves 239

4.3.1 Derivative based methods for QRS detection 239

4.3.2 The Pan-Tompkins algorithm for QRS detection 243

4.3.3 Detection of the dicrotic notch 247

4.4 Correlation Analysis of EEG Rhythms 249

4.4.1 Detection of EEG rhythms 249

4.4.2 Template matching for EEG spike and wave detection 252

4.4.3 Detection of EEG rhythms related to seizure 254

4.5 Cross-spectral Techniques 255

4.5.1 Coherence analysis of EEG channels 255

4.6 The Matched Filter 260

4.6.1 Derivation of the transfer function of the matched filter 260

4.6.2 Detection of EEG spike and wave complexes 263

4.7 Detection of the P Wave in the ECG 267

4.8 Homomorphic Filtering 269

4.8.1 Generalized linear filtering 270

4.8.2 Homomorphic deconvolution 271

4.8.3 Extraction of the vocal tract response 272

4.9 Application: ECG Rhythm Analysis 281

4.10 Application: Identification of Heart Sounds 284

4.11 Application: Detection of the Aortic Component of S2 286

4.12 Remarks 290

4.13 Study Questions and Problems 291

4.14 Laboratory Exercises and Projects 293

5 Analysis of Waveshape and Waveform Complexity 295

5.1 Problem Statement 296

5.2 Illustration of the Problem with Case Studies 296

5.2.1 The QRS complex in the case of bundle-branch block 296

5.2.2 The effect of myocardial ischemia and infarction on QRS waveshape 296

5.2.3 Ectopic beats 297

5.2.4 Complexity of the EMG interference pattern 297

5.2.5 PCG intensity patterns 297

5.3 Analysis of ERPs 298

5.4 Morphological Analysis of ECG Waves 298

5.4.1 Correlation coefficient 299

5.4.2 The minimum-phase correspondent and signal length 299

5.4.3 ECG waveform analysis 306

5.5 Envelope Extraction and Analysis 307

5.5.1 Amplitude demodulation 309

5.5.2 Synchronized averaging of PCG envelopes 311

5.5.3 The envelogram 311

5.6 Analysis of Activity 314

5.6.1 The RMS value 315

5.6.2 Zero-crossing rate 317

5.6.3 Turns count 317

5.6.4 Form factor 319

5.7 Application: Normal and Ectopic ECG Beats 320

5.8 Application: Analysis of Exercise E