About the Authors xvi -- Foreword by Prof. Willis J. Tompkins xviii -- Foreword by Prof. Alan V. Oppenheim xix -- Preface xxii -- Acknowledgments xxviii -- Symbols and Abbreviations xxxi -- About the Companion Website xxxix -- 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 5 -- 1.2.2 The action potential of a neuron 9 -- 1.2.3 The electroneurogram (ENG) 10 -- 1.2.4 The electromyogram (EMG) 12 -- 1.2.5 The electrocardiogram (ECG) 20 -- 1.2.6 The electroencephalogram (EEG) 29 -- 1.2.7 Event-related potentials (ERPs) 35 -- 1.2.8 The electrogastrogram (EGG) 36 -- 1.2.9 The phonocardiogram (PCG) 37 -- 1.2.10 The carotid pulse 40 -- 1.2.11 The photoplethysmogram (PPG) 41 -- 1.2.12 Signals from catheter-tip sensors 43 -- 1.2.13 The speech signal 44 -- 1.2.14 The vibroarthrogram (VAG) 48 -- 1.2.15 The vibromyogram (VMG) 52 -- 1.2.16 Otoacoustic emission (OAE) signals 52 -- 1.2.17 Bioacoustic signals 52 -- 1.3 Objectives of Biomedical Signal Analysis 52 -- 1.4 Challenges in Biomedical Signal Analysis 55 -- 1.5 Why Use Computer-aided Monitoring and Diagnosis? 58 -- 1.6 Remarks 60 -- 1.7 Study Questions and Problems 60 -- 1.8 Laboratory Exercises and Projects 62 -- References 63 -- 2 Analysis of Concurrent, Coupled, and Correlated Processes 71 -- 2.1 Problem Statement 71 -- 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 73 -- 2.2.4 Cardiorespiratory interaction 75 -- 2.2.5 Heart-rate variability 75 -- 2.2.6 The EMG and VMG 77 -- 2.2.7 The knee-joint and muscle-vibration signals 77 -- 2.3 Application: Segmentation of the PCG 78 -- 2.4 Application: Diagnosis and Monitoring of Sleep Apnea 79 -- 2.4.1 Monitoring of sleep apnea by polysomnography 80 -- 2.4.2 Home monitoring of sleep apnea 80 -- 2.4.3 Multivariate and multiorgan analysis 82 -- 2.5 Remarks 85 -- 2.6 Study Questions and Problems 85 -- 2.7 Laboratory Exercises and Projects 86 -- References 86 -- 3 Filtering for Removal of Artifacts 91 -- 3.1 Problem Statement 91 -- 3.2 Random, Structured, and Physiological Noise 92 -- 3.2.1 Random noise 92 -- 3.2.2 Structured noise 98 -- 3.2.3 Physiological interference 98 -- 3.2.4 Stationary, nonstationary, and cyclostationary processes 99 -- 3.3 Illustration of the Problem with Case Studies 101 -- 3.3.1 Noise in event-related potentials 102 -- 3.3.2 High-frequency noise in the ECG 102 -- 3.3.3 Motion artifact in the ECG 102 -- 3.3.4 Power-line interference in ECG signals 103 -- 3.3.5 Maternal ECG interference in fetal ECG 105 -- 3.3.6 Muscle-contraction interference in VAG signals 105 -- 3.3.7 Potential solutions to the problem 106 -- 3.4 Fundamental Concepts of Filtering 106 -- 3.4.1 Linear shift-invariant filters and convolution 107 -- 3.4.2 Transform-domain analysis of signals and systems 117 -- 3.4.3 The pole-zero plot 123 -- 3.4.4 The Fourier transform 125 -- 3.4.5 The discrete Fourier transform 126 -- 3.4.6 Convolution using the DFT 131 -- 3.4.7 Properties of the Fourier transform 133 -- 3.5 Synchronized Averaging 135 -- 3.6 Time-domain Filters 139 -- 3.6.1 Moving-average filters 139 -- 3.6.2 Derivative-based operators to remove low-frequency artifacts 145 -- 3.6.3 Various specifications of a filter 152 -- 3.7 Frequency-domain Filters 153 -- 3.7.1 Removal of high-frequency noise: Butterworth lowpass filters 154 -- 3.7.2 Removal of low-frequency noise: Butterworth highpass filters 161 -- 3.7.3 Removal of periodic artifacts: Notch and comb filters 162 -- 3.8 Order-statistic Filters 169 -- 3.9 The Wiener Filter 171 -- 3.10 Adaptive Filters for Removal of Interference 180 -- 3.10.1 The adaptive noise canceler 181 -- 3.10.2 The least-mean-squares adaptive filter 184 -- 3.10.3 The RLS adaptive filter 185 -- 3.11 Selecting an Appropriate Filter 190 -- 3.12 Application: Removal of Artifacts in ERP Signals 193 -- 3.13 Application: Removal of Artifacts in the ECG 196 -- 3.14 Application: Maternal-Fetal ECG 197 -- 3.15 Application: Muscle-contraction Interference 199 -- 3.16 Remarks 202 -- 3.17 Study Questions and Problems 202 -- 3.18 Laboratory Exercises and Projects 208 -- References 209 -- 4 Detection of Events 213 -- 4.1 Problem Statement 213 -- 4.2 Illustration of the Problem with Case Studies 214 -- 4.2.1 The P, QRS, and T waves in the ECG 214 -- 4.2.2 The first and second heart sounds 215 -- 4.2.3 The dicrotic notch in the carotid pulse 215 -- 4.2.4 EEG rhythms, waves, and transients 215 -- 4.3 Detection of Events and Waves 218 -- 4.3.1 Derivative-based methods for QRS detection 218 -- 4.3.2 The Pan-Tompkins algorithm for QRS detection 220 -- 4.3.3 Detection of the P wave in the ECG 224 -- 4.3.4 Detection of the T wave in the ECG 226 -- 4.3.5 Detection of the dicrotic notch 228 -- 4.4 Correlation Analysis of EEG Rhythms 228 -- 4.4.1 Detection of EEG rhythms 228 -- 4.4.2 Template matching for EEG spike-and-wave detection 231 -- 4.4.3 Detection of EEG rhythms related to seizure 234 -- 4.5 Cross-spectral Techniques 235 -- 4.5.1 Coherence analysis of EEG channels 235 -- 4.6 The Matched Filter 237 -- 4.6.1 Derivation of the transfer function of the matched filter 237 -- 4.6.2 Detection of EEG spike-and-wave complexes 241 -- 4.7 Homomorphic Filtering 242 -- 4.7.1 Generalized linear filtering 244 -- 4.7.2 Homomorphic deconvolution 244 -- 4.7.3 Extraction of the vocal-tract response 245 -- 4.8 Application: ECG Rhythm Analysis 253 -- 4.9 Application: Identification of Heart Sounds 254 -- 4.10 Application: Detection of the Aortic Component of S 2 256 -- 4.11 Remarks 259 -- 4.12 Study Questions and Problems 259 -- 4.13 Laboratory Exercises and Projects 261 -- References 262 -- 5 Analysis of Waveshape and Waveform Complexity 267 -- 5.1 Problem Statement 267 -- 5.2 Illustration of the Problem with Case Studies 268 -- 5.2.1 The QRS complex in the case of bundle-branch block 268 -- 5.2.2 The effect of myocardial ischemia on QRS waveshape 268 -- 5.2.3 Ectopic beats 268 -- 5.2.4 Complexity of the EMG interference pattern 268 -- 5.2.5 PCG intensity patterns 269 -- 5.3 Analysis of ERPs 269 -- 5.4 Morphological Analysis of ECG Waves 269 -- 5.4.1 Correlation coefficient 270 -- 5.4.2 The minimum-phase correspondent and signal length 270 -- 5.4.3 ECG waveform analysis 274 -- 5.5 Envelope Extraction and Analysis 277 -- 5.5.1 Amplitude demodulation 278 -- 5.5.2 Synchronized averaging of PCG envelopes 280 -- 5.5.3 The envelogram 281 -- 5.6 Analysis of Activity 283 -- 5.6.1 The RMS value 283 -- 5.6.2 Zero-crossing rate 285 -- 5.6.3 Turns count 285 -- 5.6.4 Form factor 286 -- 5.7 Application: Normal and Ectopic ECG Beats 287 -- 5.8 Application: Analysis of Exercise ECG 288 -- 5.9 Application: Analysis of the EMG in Relation to Force 290 -- 5.10 Application: Analysis of Respiration 292 -- 5.11 Application: Correlates of Muscular Contraction 294 -- 5.12 Application: Statistical Analysis of VAG Signals 295 -- 5.12.1 Acquisition of knee-joint VAG signals 297 -- 5.12.2 Estimation of the PDFs of VAG signals 297 -- 5.12.3 Screening of VAG signals using statistical parameters 299 -- 5.13 Application: Fractal Analysis of the EMG in Relation to Force 302 -- 5.13.1 Fractals in nature 302 -- 5.13.2 Fractal dimension 303 -- 5.13.3 Fractal analysis of physiological signals 304 -- 5.13.4 Fractal analysis of EMG signals 305 -- 5.14 Remarks 306 -- 5.15 Study Questions and Problems 307 -- 5.16 Laboratory Exercises and Projects 309 -- References 310 -- 6 Frequency-domain Characterization of Signals and Systems 317 -- 6.1 Problem Statement 318 -- 6.2 Illustration of the Problem with Case Studies 318 -- 6.2.1 The effect of myocardial elasticity on heart sound spectra 318 -- 6.2.2 Frequency analysis of murmurs to diagnose valvular defects 319 -- 6.3 Estimation of the PSD 321 -- 6.3.1 Considerations in the computation of the ACF 321 -- 6.3.2 The periodogram 323 -- 6.3.3 The need for averaging PSDs 325 -- 6.3.4 The use of windows: spectral resolution and leakage 326 -- 6.3.5 Estimation of the ACF from the PSD 330 -- 6.3.6 Synchronized averaging of PCG spectra 331 -- 6.4 Measures Derived from PSDs 333 -- 6.4.1 Moments of PSD functions 334 -- 6.4.2 Spectral power ratios 337 -- 6.5 Application: Evaluation of Prosthetic Heart Valves 337 -- 6.6 Application: Fractal Analysis of VAG Signals 339 -- 6.6.1 Fractals and the 1/f model 339 -- 6.6.2 F D via power

spectral analysis 341 -- 6.6.3 Examples of synthesized fractal signals 341 -- 6.6.4 Fractal analysis of segments of VAG signals 342 -- 6.7 Application: Spectral Analysis of EEG Signals 345 -- 6.8 Remarks 349 -- 6.9 Study Questions and Problems 350 -- 6.10 Laboratory Exercises and Projects 351 -- References 353 -- 7 Modeling of Biomedical Signal-generating Processes and Systems 357 -- 7.1 Problem Statement 357 -- 7.2 Illustration of the Problem 358 -- 7.2.1 Motor-unit firing patterns 358 -- 7.2.2 Cardiac rhythm 358 -- 7.2.3 Formants and pitch in speech 359 -- 7.2.4 Patellofemoral crepitus 360 -- 7.3 Point Processes 360 -- 7.4 Parametric System Modeling 365 -- 7.5 Autoregressive or All-pole Modeling 369 -- 7.5.1 Spectral matching and parameterization 374 -- 7.5.2 Optimal model order 377 -- 7.5.3 AR and cepstral coefficients 384 -- 7.6 Pole-Zero Modeling 384 -- 7.6.1 Sequential estimation of poles and zeros 387 -- 7.6.2 Iterative system identification 389 -- 7.6.3 Homomorphic prediction and modeling 393 -- 7.7 Electromechanical Models of Signal Generation 395 -- 7.7.1 Modeling of respiratory sounds 396 -- 7.7.2 Modeling sound generation in coronary arteries 400 -- 7.7.3 Modeling sound generation in knee joints 402 -- 7.8 Electrophysiological Models of the Heart 404 -- 7.8.1 Electrophysiologica ...