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
Autorentext
Rangaraj M. Rangayyan, PhD, is Professor in the Department of Electrical and Computer Engineering and an Adjunct Professor of Surgery and Radiology at the University of Calgary in Calgary, Canada. Dr. Rangayyan has published over 150 papers in journals and 250 papers in conference proceedings, and has authored two textbooks, Biomedical Signal Analysis (Wiley-IEEE Press 2002/2015) and Biomedical Image Analysis (CRC Press 2005). He has been recognized with the 2013 IEEE Canada Outstanding Engineer Medal, and elected as a Fellow of the IEEE, Canadian Medical and Biological Engineering Society, the American Institute for Medical and Biological Medical Engineering, and other societies.
Klappentext
Biomedical Signal Analysis, Second Edition uses a pedagogical and illustrative approach to introduce various signal analysis techniques that are particularly important for biomedical applications.
The book presents digital signal processing and pattern recognition techniques for analysis of biomedical signals. It begins with an introduction on the nature of biomedical signals, such as the action potential, electrocardiogram, muscle signals, brain signals, heart sounds, and speech. A detailed review of signals and systems is presented to set the stage for filtering of biomedical signals to remove noise and various artifacts. Several filtering techniques are presented with applications, from simple averaging to advanced and sophisticated optimal filtering methods. Techniques for detection and characterization of events and waves within a given signal are described. Several advanced techniques are described for adaptive analysis of non-stationary signals using time-frequency, wavelet, and other forms of representation. The book concludes with a chapter on pattern classification techniques that could be used in diagnostic decision-making procedures.
The new edition of this book includes:
- End-of-chapter study questions, problems, and laboratory exercises
- Details on the z-transform, the Fourier Transform, random processes, and linear filters and their characteristics
- Methods for analysis of muscle, heart, brain, and knee-joint signals
- Methods for pattern analysis and classification with illustrations of application to biomedical signals
- Fractal analysis with biomedical applications
This book will help assist readers in the development of techniques for analysis of biomedical signals and computer-aided diagnosis.
Rangaraj M. Rangayyan, PhD, is Professor in the Department of Electrical and Computer Engineering and an Adjunct Professor of Surgery and Radiology at the University of Calgary in Calgary, Canada. Dr. Rangayyan has published over 150 papers in journals and 250 papers in conference proceedings, and has authored two textbooks, Biomedical Signal Analysis (Wiley-IEEE Press 2002/2015) and Biomedical Image Analysis (CRC Press 2005). He has been recognized with the 2013 IEEE Canada Outstanding Engineer Medal, and elected as a Fellow of the IEEE, Canadian Medical and Biological Engineering Society, the American Institute for Medical and Biological Medical Engineering, and other societies.
Inhalt
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 multiorgan 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 even-trelated potentials 104
3.3.2 High frequency noise in the ECG 104
3.3.3 Motion artifact in the ECG 104
3.3.4 Power-line 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-invariant filters 112
3.4.2 Transform-domain analysis of signals and systems 124
3.4.3 The polezero 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 lowpass 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-means-quares adaptive filter 20...