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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: IEEE Press Series on Biomedical Engineering
- Sprache: Englisch
Biomedical Signal Analysis Comprehensive resource covering recent developments, applications of current interest, and advanced techniques for biomedical signal analysis Biomedical Signal Analysis provides extensive insight into digital signal processing techniques for filtering, identification, characterization, classification, and analysis of biomedical signals with the aim of computer-aided diagnosis, taking a unique approach by presenting case studies encountered in the authors' research work. Each chapter begins with the statement of a biomedical signal problem, followed by a selection of real-life case studies and illustrations with the associated signals. Signal processing, modeling, or analysis techniques are then presented, starting with relatively simple "textbook" methods, followed by more sophisticated research-informed approaches. Each chapter concludes with solutions to practical applications. Illustrations of real-life biomedical signals and their derivatives are included throughout. The third edition expands on essential background material and advanced topics without altering the underlying pedagogical approach and philosophy of the successful first and second editions. The book is enhanced by a large number of study questions and laboratory exercises as well as an online repository with solutions to problems and data files for laboratory work and projects. Biomedical Signal Analysis provides theoretical and practical information on: * The origin and characteristics of several biomedical signals * Analysis of concurrent, coupled, and correlated processes, with applications in monitoring of sleep apnea * Filtering for removal of artifacts, random noise, structured noise, and physiological interference in signals generated by stationary, nonstationary, and cyclostationary processes * Detection and characterization of events, covering methods for QRS detection, identification of heart sounds, and detection of the dicrotic notch * Analysis of waveshape and waveform complexity * Interpretation and analysis of biomedical signals in the frequency domain * Mathematical, electrical, mechanical, and physiological modeling of biomedical signals and systems * Sophisticated analysis of nonstationary, multicomponent, and multisource signals using wavelets, time-frequency representations, signal decomposition, and dictionary-learning methods * Pattern classification and computer-aided diagnosis Biomedical Signal Analysis is an ideal learning resource for senior undergraduate and graduate engineering students. Introductory sections on signals, systems, and transforms make this book accessible to students in disciplines other than electrical engineering.
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
DEDICATION
ABOUT THE AUTHORS
FOREWORD BY PROF. WILLIS J. TOMPKINS
FOREWORD BY PROF. ALAN V. OPPENHEIM
PREFACE
From the Second to the Third Edition
Excerpts from the Preface to the Second Edition
Excerpts from the Preface to the First Edition: Background and Motivation
ACKNOWLEDGMENTS
Acknowledgments: Third Edition
Acknowledgments: Second Edition
Acknowledgments: First Edition
SYMBOLS AND ABBREVIATIONS
ABOUT THE COMPANION WEBSITE
CHAPTER 1: INTRODUCTION TO BIOMEDICAL SIGNALS
1.1 The Nature of Biomedical Signals
1.2 Examples of Biomedical Signals
1.3 Objectives of Biomedical Signal Analysis
1.4 Challenges in Biomedical Signal Acquisition and Analysis
1.5 Why Use Computer-aided Monitoring and Diagnosis?
1.6 Remarks
1.7 Study Questions and Problems
1.8 Laboratory Exercises and Projects
References
CHAPTER 2: ANALYSIS OF CONCURRENT, COUPLED, AND CORRELATED PROCESSES
2.1 Problem Statement
2.2 Illustration of the Problem with Case Studies
2.3 Application: Segmentation of the PCG into Systolic and Diastolic Parts
2.4 Application: Diagnosis and Monitoring of Sleep Apnea
2.5 Remarks
2.6 Study Questions and Problems
2.7 Laboratory Exercises and Projects
References
CHAPTER 3: FILTERING FOR REMOVAL OF ARTIFACTS
3.1 Problem Statement
3.2 Random, Structured, and Physiological Noise
3.3 Illustration of the Problem with Case Studies
3.4 Fundamental Concepts of Filtering
3.5 Synchronized Averaging
3.6 Time-domainFilters
3.7 Frequency-domainFilters
3.8 Order-statistic Filters
3.9 The WienerFilter
3.10 Adaptive Filters for Removal of Interference
3.11 Selecting an Appropriate Filter
3.12 Application: Removal of Artifacts in ERP Signals
3.13 Application: Removal of Artifacts in the ECG
3.14 Application: Adaptive Cancellation of the Maternal ECG to Obtain the Fetal ECG
3.15 Application: Adaptive Cancellation of Muscle-contraction Interference in VAG Signals
3.16 Remarks
3.17 Study Questions and Problems
3.18 Laboratory Exercises and Projects
References
CHAPTER 4: DETECTION OF EVENTS
4.1 Problem Statement
4.2 Illustration of the Problem with Case Studies
4.3 Detection of Events and Waves
4.4 Correlation Analysis of EEGRhythms
4.5 Cross-spectral Techniques
4.6 The MatchedFilter
4.7 Homomorphic Filtering and the ComplexCepstrum
4.8 Application: ECG Rhythm Analysis
4.9 Application: Identification of Heart Sounds
4.10 Application: Detection of the Aortic Component of the Second HeartSound
4.11 Remarks
4.12 Study Questions and Problems
4.13 Laboratory Exercises and Projects
References
CHAPTER 5: ANALYSIS OF WAVESHAPE AND WAVEFORM COMPLEXITY
5.1 Problem Statement
5.2 Illustration of the Problem with Case Studies
5.3 Analysis of ERPs
5.4 Morphological Analysis of ECG Waves
5.5 Envelope Extraction and Analysis
5.6 Analysis of Activity
5.7 Application: Parameterization of Normal and Ectopic ECG Beats
5.8 Application: Analysis of Exercise ECG
5.9 Application: Quantitative Analysis of the EMG in Relation to Force Exerted
5.10 Application: Analysis of Respiration
5.11 Application: Electrical and Mechanical Correlates of Muscular Contraction
5.12 Application: Statistical Analysis of VAG Signals
5.13 Application: Fractal Analysis of the EMG in Relation to Force
5.14 Remarks
5.15 Study Questions and Problems
5.16 Laboratory Exercises and Projects
References
CHAPTER 6: FREQUENCY-DOMAIN CHARACTERIZATION OF SIGNALS AND SYSTEMS
6.1 Problem Statement
6.2 Illustration of the Problem with Case Studies
6.3 Estimation of the PSD
6.4 Measures Derived from Power Spectral Density Functions
6.5 Application: Evaluation of Prosthetic Heart Valves
6.6 Application: Fractal Analysis ofVAGSignals
6.7 Application: Spectral Analysis of EEG Signals
6.8 Remarks
6.9 Study Questions and Problems
6.10 Laboratory Exercises and Projects
References
CHAPTER 7: MODELING OF BIOMEDICAL SIGNAL-GENERATING PROCESSES AND SYSTEMS
7.1 Problem Statement
7.2 Illustration of the Problem with Case Studies
7.3 Point Processes
7.4 Parametric System Modeling
7.5 Autoregressive or All-poleModeling
7.6 Pole–Zero Modeling
7.7 Electromechanical Models of Signal Generation
7.8 Electrophysiological Models of the Heart
7.9 Application: Analysis of Heart-rateVariability
7.10 Application: Spectral Modeling and Analysis of PCG Signals
7.11 Application: Detection of Coronary ArteryDisease
7.12 Remarks
7.13 Study Questions and Problems
7.14 Laboratory Exercises and Projects
References
CHAPTER 8: ADAPTIVE ANALYSIS OF NONSTATIONARY SIGNALS
8.1 Problem Statement
8.2 Illustration of the Problem with Case Studies
8.3 Time-variant Systems
8.4 Fixed Segmentation
8.5 Adaptive Segmentation
8.6 Use of Adaptive Filters forSegmentation
8.7 The Kalman Filter
8.8 Wavelet Analysis
8.9 Bilinear TFDs
8.10 Application: Adaptive Segmentation of EEGSignals
8.11 Application: Adaptive Segmentation of PCGSignals
8.12 Application: Time-varying Analysis of HRV
8.13 Application: Analysis of Crying Sounds of Infants
8.14 Application: Wavelet Denoising of PPG Signals
8.15 Application: Wavelet Analysis for CPR Studies
8.16 Application: Detection of Ventricular Fibrillation in ECG Signals
8.17 Application: Detection of Epileptic Seizures in EEG Signals
8.18 Application: Neural Decoding for Control of Prostheses
8.19 Remarks
8.20 Study Questions and Problems
8.21 Laboratory Exercises and Projects
References
CHAPTER 9: SIGNAL ANALYSIS VIA ADAPTIVE DECOMPOSITION
9.1 Problem Statement
9.2 Illustration of the Problem with Case Studies
9.3 Matching Pursuit
9.4 Empirical Mode Decomposition
9.5 Dictionary Learning
9.6 Decomposition-based Adaptive TFD
9.7 Separation of Mixtures of Signals
9.8 Application: Detection of Epileptic Seizures Using Dictionary Learning Methods
9.9 Application: Adaptive Time–Frequency Analysis of VAG Signals
9.10 Application: Detection of T-wave Alternans in ECGSignals
9.11 Application: Extraction of the Fetal ECG from Single-channel Maternal ECG
9.12 Application: EEG Analysis for Brain–ComputerInterfaces
9.13 Remarks
9.14 Study Questions and Problems
9.15 Laboratory Exercises and Projects
References
CHAPTER 10: COMPUTER-AIDED DIAGNOSIS AND HEALTHCARE
10.1 Problem Statement
10.2 Illustration of the Problem with Case Studies
10.3 Pattern Classification
10.4 Supervised Pattern Classification
10.5 Unsupervised Pattern Classification
10.6 Probabilistic Models and StatisticalDecision
10.7 Logistic Regression Analysis
10.8 Neural Networks
10.9 Measures of Diagnostic Accuracy andCost
10.10 Reliability of Features, Classifiers, and Decisions
10.11 Application: Normal versus Ectopic ECG Beats
10.12 Application: Detection of Knee-joint Cartilage Pathology
10.13 Application: Detection of Sleep Apnea
10.14 Application: Monitoring Parkinson’s Disease Using Multimodal Signal Analysis
10.15 Strengths and Limitations of CAD
10.16 Remarks
10.17 Study Questions and Problems
10.18 Laboratory Exercises and Projects
References
Index
End User License Agreement
List of Tables
Chapter 9
Table 9.1 Matrix decomposition performance for TF quantification
Chapter 10
Table 10.1 Schematic representation of a classification matrix. denotes th...
Table 10.2 Schematic representation of the cost matrix of a diagnostic metho...
Table 10.3 Schematic representation of a contingency table for McNemar’s tes...
Table 10.4 Contingency table for a method of sonification of VAG signals ver...
Table 10.5 Contingency table for a method of sonification of VAG signals ver...
Table 10.6 Causes of various types of errors in manual analysis of biomedica...
Table 10.7 Comparison of various aspects of manual versus computer analysis ...
Table 10.8 Techniques and means to move from manual to computer analysis of ...
Table 10.9 Training set of feature vectors
Table 10.10 Test set of feature vectors
List of Illustrations
Chapter 1
Figure 1.1 Schematic representation of a generic physiological system with v...
Figure 1.2 Measurements of the temperature of a patient presented as (a) a s...
Figure 1.3 Measurements of the BP of a patient presented as (a) a single pai...
Figure 1.4 Schematic representation of a cell and its characteristics. The p...
Figure 1.5 Schematic representation (a) of a cell in its resting or polarize...
Figure 1.6 Illustration of the various phases or intervals of the action pot...
Figure 1.7 Action potentials of rabbit ventricular and atrial myocytes. Data...
Figure 1.8 A single ventricular myocyte of a rabbit in its (a) relaxed state...
Figure 1.9 Schematic representation of a cell as a system. Upon receiving an...
Figure 1.10 Schematic representation of a neuron.
Figure 1.11 First known tracing of an action potential recorded from the axo...
Figure 1.12 Neuronal action potentials recorded from a rat’s brain using a m...
Figure 1.13 Nerve conduction velocity (NCV) measurement via electrical stimu...
Figure 1.14 Schematic representation of two motor units, one in solid line a...
Figure 1.15 Schematic representation of a motor unit and model for the gener...
Figure 1.16 Schematic representations of monophasic, biphasic, and triphasic...
Figure 1.17 SMUAP trains recorded simultaneously from three channels of need...
Figure 1.18 Examples of SMUAP trains. (a) From the right deltoid of a normal...
Figure 1.19 Schematic representation of spatiotemporal recruitment of motor ...
Figure 1.20 EMG signal recorded from the crural diaphragm muscle of a dog us...
Figure 1.21 The initial part of the EMG signal in Figure 1.20 shown on an ex...
Figure 1.22 Force signal (upper plot) and the EMG signal (lower plot) record...
Figure 1.23 Expanded view of the part of the EMG (lower plot) and force sign...
Figure 1.24 Schematic representation of the chambers, valves, vessels, and c...
Figure 1.25 Propagation of the excitation pulse through the heart. Reproduce...
Figure 1.26 Schematic representations of an ECG signal and the action potent...
Figure 1.27 A typical ECG signal (male subject of age years). (
Note
: Signa...
Figure 1.28 Summary of the parts and waves of a cardiac cycle as seen in an ...
Figure 1.29 ECG signal with PVCs. The third and sixth beats are PVCs. The fi...
Figure 1.30 ECG signal of a patient with right bundle-branch block and hyper...
Figure 1.31 Limb leads used to acquire the commonly used lead II ECG.
Note
: ...
Figure 1.32 Einthoven’s triangle and the axes of the six ECG leads formed by...
Figure 1.33 Positions for placement of the precordial (chest) leads V1–V6 fo...
Figure 1.34 Vectorial relations between ECG leads I, II, and III. See also F...
Figure 1.35 Vectorial relations between ECG leads I, III, and aVL. See also ...
Figure 1.36 Standard 12-lead ECG of a normal male adult. Signal courtesy of ...
Figure 1.37 Standard 12-lead ECG of a patient with right bundle-branch block...
Figure 1.38 Schematic diagram showing the various parts and functional areas...
Figure 1.39 The system of electrode placement for EEG recording [46]. Note...
Figure 1.40 From top to bottom: (a) delta rhythm; (b) theta rhythm; (c) alph...
Figure 1.41 Eight channels of the EEG of a subject displaying alpha rhythm. ...
Figure 1.42 Ten channels of the EEG of a subject displaying spike-and-wave c...
Figure 1.43 channels of the EEG of a subject displaying seizure activity. ...
Figure 1.44 Three-channel simultaneous record of the PCG, ECG, and carotid p...
Figure 1.45 Schematic representation of the genesis of heart sounds. Only th...
Figure 1.46 Three-channel simultaneous record of the PCG, ECG, and carotid p...
Figure 1.47 PPG signals obtained from multiple body sites (left and right ea...
Figure 1.48 From top to bottom: PPG signal; respiratory signal obtained usi...
Figure 1.49 Normal ECG and intracardiac pressure signals from a dog. The pre...
Figure 1.50 ECG and intracardiac pressure signals from a dog with PVCs. The ...
Figure 1.51 Schematic diagram of the anatomy of the vocal tract.
Figure 1.52 Schematic representation of the speech production system.
Figure 1.53 Schematic representation of the production of voiced and unvoice...
Figure 1.54 Speech signal of the word “safety” uttered by a male speaker. Ap...
Figure 1.55 Segments of the speech signal in Figure 1.54 on an expanded scal...
Figure 1.56 Front and side views of the knee joint (the two views are not mu...
Figure 1.57 Experimental setup to measure VAG and the related muscle-contrac...
Figure 1.58 Left-hand column: VMG signals recorded simultaneously at (top-to...
Figure 1.59 Computer-aided diagnosis and therapy based on biomedical signal ...
Chapter 2
Figure 2.1 Pill-electrode recording of the atrial electrogram (lower tracing...
Figure 2.2 Atrial electrogram (lower tracing) and the external ECG (upper tr...
Figure 2.3 ECG signal of a subject (a) with the subject breathing normally, ...
Figure 2.4 Simultaneous EMG – VMG records at two levels of contraction of th...
Figure 2.5 Demarcation of the systolic (SYS.) and diastolic (DIAS.) parts of...
Figure 2.6 Top to bottom: EEG (F4), submental EMG (SME), leg EMG, ECG, airfl...
Figure 2.7 Top to bottom: Heart rate, , nasal pressure, and snoring sound s...
Figure 2.8 Top to bottom: Heart rate, , nasal pressure, and snoring sound s...
Figure 2.9 Top to bottom: EEG, EMG from the tibia, RR intervals, and respira...
Chapter 3
Figure 3.1 Top: Speech signal of the word “safety” uttered by a male speaker...
Figure 3.2 Ten sample acquisitions ( to ) of individual flash visual ERPs ...
Figure 3.3 Top: Speech signal of the word “safety” uttered by a male speaker...
Figure 3.4 Spectrogram of the speech signal of the word “safety” uttered by ...
Figure 3.5 ECG signal with high-frequency noise.
Figure 3.6 ECG signal with low-frequency artifact.
Figure 3.7 ECG signal with power-line () interference.
Figure 3.8 Fourier power spectrum of the ECG signal in Figure 3.7 with power...
Figure 3.9 ECG signals of a pregnant woman from abdominal and chest leads: (...
Figure 3.10 Schematic representation of the delta function as a limit of a r...
Figure 3.11 The delta function as the limit of as . The function is plott...
Figure 3.12 A schematic representation of a continuous-time or discrete-time...
Figure 3.13 A schematic representation of the discrete-time unit impulse fun...
Figure 3.14 A schematic representation of the discrete-time unit step functi...
Figure 3.15 A schematic representation of the impulse response, , of a disc...
Figure 3.16 A numerical illustration of shifted versions of signal. Blank sp...
Figure 3.17 A schematic illustration of reversing and shifting of a signal....
Figure 3.18 A numerical illustration of the convolution of two discrete-time...
Figure 3.19 A numerical illustration of the convolution of two discrete-time...
Figure 3.20 Top to bottom: original signal as in Equation 3.40; Gaussian-d...
Figure 3.21 Histogram of a realization of the noise process used in the exam...
Figure 3.22 Top to bottom: original signal as in Equation 3.40; Gaussian-d...
Figure 3.23 The linear ramp filter in Equation 3.42 is shown superimposed (i...
Figure 3.24 Two LSI systems in series and the equivalent system.
Figure 3.25 Two LSI systems in parallel and the equivalent system.
Figure 3.26 Schematic representation of the -plane or Laplace transform dom...
Figure 3.27 Relationship among a signal and its sampled discrete-time form...
Figure 3.28 Input–output relationship for an LTI system in the -domain or a...
Figure 3.29 Two LSI systems in series and the equivalent system in the -dom...
Figure 3.30 Two LSI systems in parallel and the equivalent system in the -d...
Figure 3.31 Transformation from the Laplace domain to the -domain with .
Figure 3.32 Interpretation of the frequency variable with uniform sampling a...
Figure 3.33 Derivation of the frequency response of a system from its pole–z...
Figure 3.34 Vectors (or phasors) representing the roots of unity, with ,
Figure 3.35 Basis functions used in the DFT. The eight plots (top to bottom)...
Figure 3.36 Basis functions used in the DFT. The eight plots (top to bottom)...
Figure 3.37 Basis functions used in the DFT. The eight plots (top to bottom)...
Figure 3.38 Samples of the DFT around the unit circle in the -plane with ...
Figure 3.39 A numerical illustration of circularly shifted versions of a per...
Figure 3.40 A numerical illustration of circular or periodic convolution of ...
Figure 3.41 A numerical illustration of the undesired effect of periodic con...
Figure 3.42 A numerical illustration of the use of periodic convolution of t...
Figure 3.43 Traces 1 and 2: Two sample acquisitions of individual flash visu...
Figure 3.44 An ECG signal with noise (upper trace) and the result of cross-c...
Figure 3.45 Upper two traces: two cycles of the ECG extracted from the signa...
Figure 3.46 Signal-flow diagram of an MA filter of order . Each block with ...
Figure 3.47 Signal-flow diagram of the Hann filter.
Figure 3.48 Impulse response of the Hann filter.
Figure 3.49 Magnitude and phase responses of the Hann (smoothing) filter.
Figure 3.50 Magnitude and phase responses of the 8-point MA (smoothing) filt...
Figure 3.51 Pole–zero plot of the 8-point MA (smoothing) filter; .
Figure 3.52 ECG signal with high-frequency noise; .
Figure 3.53 The ECG signal with high-frequency noise in Figure 3.52 after fi...
Figure 3.54 Magnitude and phase responses of the first-order difference oper...
Figure 3.55 Magnitude and phase responses of the three-point central-differe...
Figure 3.56 Result of filtering the ECG signal with low-frequency noise show...
Figure 3.57 Result of filtering the ECG signal with low-frequency noise show...
Figure 3.58 Two equivalent signal-flow diagrams of the filter to remove low-...
Figure 3.59 Graphical evaluation of the frequency response of a highpass fil...
Figure 3.60 Normalized magnitude and phase responses of the filter to remove...
Figure 3.61 Result of processing the ECG signal with low-frequency noise sho...
Figure 3.62 Signal-flow diagram of a direct realization of a generic IIR fil...
Figure 3.63 Signal-flow diagram of a realization of an IIR filter that uses ...
Figure 3.64 Pole positions on the Butterworth circle in the -plane of the s...
Figure 3.65 Positions of the poles and zeros with reference to the unit circ...
Figure 3.66 Magnitude response of the Butterworth lowpass filter with , , ...
Figure 3.67 Magnitude responses of three Butterworth lowpass filters with ,...
Figure 3.68 Upper trace: a carotid pulse signal with high-frequency noise. L...
Figure 3.69 Result of frequency-domain filtering of the noisy ECG signal in ...
Figure 3.70 Frequency response of the eighth-order Butterworth lowpass filte...
Figure 3.71 Result of frequency-domain filtering of the ECG signal with low-...
Figure 3.72 Frequency response of an eighth-order Butterworth highpass filte...
Figure 3.73 Zeros of the notch filter on the unit circle in the -domain to ...
Figure 3.74 Magnitude and phase responses of the notch filter with zeros a...
Figure 3.75 Zeros of the comb filter on the unit circle in the -domain to r...
Figure 3.76 Magnitude and phase responses of the comb filter with zeros as s...
Figure 3.77 ECG signal with interference.
Figure 3.78 The ECG signal in Figure 3.77 after filtering with the notch f...
Figure 3.79 An ECG signal with power-line interference at .
Figure 3.80 Dashed line: frequency response (magnitude, in ) of a notch fil...
Figure 3.81 The result of filtering the noisy signal in Figure 3.79 using th...
Figure 3.82 The result of filtering the noisy signal in Figure 3.79 using th...
Figure 3.83 (a) A synthesized test signal with a rectangular pulse. (b) Test...
Figure 3.84 (a) A synthesized test signal with two rectangular pulses. (b) D...
Figure 3.85 (a) An ECG signal with noise. Result of filtering the ECG signal...
Figure 3.86 Signal-flow diagram of the Wiener filter.
Figure 3.87 From top to bottom: one cycle of the noisy ECG signal in Figure ...
Figure 3.88 From top to bottom: log PSD (in ) of the given noisy signal (la...
Figure 3.89 From top to bottom: one cycle of the noise-free ECG of a subject...
Figure 3.90 From top to bottom: a segment of the noisy ECG to be filtered an...
Figure 3.91 From top to bottom: log PSD (in ) of the noise‐free ECG (model)...
Figure 3.92 Block diagram of a generic ANC.
Figure 3.93 LMS-filtered versions of the VAG signals recorded from the midpa...
Figure 3.94 General structure of the adaptive RLS filter.
Figure 3.95 (a) VAG signal of a normal subject. (b) Muscle-contraction inter...
Figure 3.96 Spectrogram of the VAG signal in Figure 3.95 (a). A Hann window ...
Figure 3.97 Spectrogram of the muscle-contraction interference signal in Fig...
Figure 3.98 Spectrogram of the RLS-filtered VAG signal in Figure 3.95 (d). A...
Figure 3.99 Superimposed plots of cortical ERPs related to electrical stim...
Figure 3.100 Result of synchronized averaging of all of the ERPs shown in ...
Figure 3.101 Results of synchronized averaging of selected sets of the ERPs ...
Figure 3.102 ECG signal with a combination of artifacts and its filtered ver...
Figure 3.103 Top and bottom plots: Power spectra of the ECG signals in the t...
Figure 3.104 Result of adaptive cancellation of the maternal chest ECG from ...
Figure 3.105 Top to bottom: (a) VAG signal of a subject with chondromalacia ...
Figure 3.106 Spectrogram of the original VAG signal in Figure 3.105 (a). A H...
Figure 3.107 Spectrogram of the muscle-contraction interference signal in Fi...
Figure 3.108 Spectrogram of the RLS-filtered VAG signal in Figure 3.105 (d)....
Chapter 4
Figure 4.1 Top to bottom: (a) the K-complex; (b) the lambda wave; (c) the mu...
Figure 4.2 From top to bottom: two cycles of a filtered version of the ECG s...
Figure 4.3 From top to bottom: two cycles of a filtered version of the ECG s...
Figure 4.4 Block diagram of the Pan–Tompkins algorithm for QRS detection.
Figure 4.5 Frequency response (magnitude, in ) of the filters used in the P...
Figure 4.6 The relationship of a QRS complex to the moving-window integrator...
Figure 4.7 Results of the Pan–Tompkins algorithm. From top to bottom: two cy...
Figure 4.8 Illustration of the results at various stages of the Hengeveld an...
Figure 4.9 Detection of the P, QRS, and T waves in a three-channel ECG signa...
Figure 4.10 Two cycles of a carotid pulse signal and the result of the Lehne...
Figure 4.11 Upper trace: ACF of the portion of the p4 channel of the EEG s...
Figure 4.12 Upper trace: ACF of the portion of the f3 channel of the EEG s...
Figure 4.13 Upper trace: CCF between the portions of the p3 and p4 channel...
Figure 4.14 Upper trace: CCF between the portions of the o2 and c4 channel...
Figure 4.15 Upper trace: CCF between the portions of the f3 and f4 channel...
Figure 4.16 Upper trace: the c3 channel of the EEG signal shown in Figure 1....
Figure 4.17 Upper trace: the f3 channel of the EEG signal shown in Figure 1....
Figure 4.18 The evolution of various rhythms related to a seizure event as s...
Figure 4.19 Top: A test signal with three similar events or an event with tw...
Figure 4.21 Upper trace: The c3 channel of the EEG signal shown in Figure 1....
Figure 4.22 Upper trace: The f3 channel of the EEG signal shown in Figure 1....
Figure 4.20 Upper trace: The spike-and-wave complex between and in the c...
Figure 4.23 Operations involved in a multiplicative homomorphic system or fi...
Figure 4.24 Operations involved in a homomorphic filter for convolved signal...
Figure 4.25 Detailed block diagram of the steps involved in deconvolution of...
Figure 4.26 From top to bottom: a composite signal with a wavelet and an ech...
Figure 4.27 From top to bottom: a segment of a voiced speech signal over six...
Figure 4.28 Results of the Pan–Tompkins algorithm. Top: lowpass-filtered ver...
Figure 4.29 Results of the Pan–Tompkins algorithm with a noisy ECG signal. F...
Figure 4.30 Results of segmentation of a PCG signal into systolic and diasto...
Figure 4.31 Results of segmentation of a PCG signal into systolic and diasto...
Figure 4.32 Synchronized averaging of S2 to detect A2 and suppress P2. The f...
Chapter 5
Figure 5.1 (a) A normal ECG beat and (b)–(d) three ectopic beats (PVCs) of a...
Figure 5.2 (a) Plot of and values of several beats of a patient with mul...
Figure 5.3 ECG waveform features used by Cox et al. [14] and Nolle [15].
Figure 5.4 Use of four features to catalog QRS complexes into one of dynamic...
Figure 5.5 Averaged envelopes of the PCG signals of a normal subject and patie...
Figure 5.6 Decision tree to classify systolic murmurs based on envelope analys...
Figure 5.7 Top to bottom: PCG signal of a normal subject (male, years); enve...
Figure 5.8 Top to bottom: PCG signal of a patient (female, months) with syst...
Figure 5.9 Differences between zero-crossings, turning points, and turns count...
Figure 5.10 Top to bottom: EMG signal over two breath cycles from the crural d...
Figure 5.11 Illustration of the detection of turns in a window of the EMG si...
Figure 5.12 Segment of the ECG of a patient (male, years) with ectopic bea...
Figure 5.13 Force and EMG signals recorded from the forearm muscle of a subj...
Figure 5.14 Bottom to top: EMG signal, cumulative count of zero-crossings, a...
Figure 5.15 Variation of the value of the EMG signal in Figure 5.13 with t...
Figure 5.16 Variation of the of the EMG signal in Figure 5.13 with the ave...
Figure 5.17 Variation of the of the EMG signal in Figure 5.13 with the ave...
Figure 5.18 Top to bottom: EMG signal over two breath cycles from the parast...
Figure 5.19 Correlation between EMG amplitude obtained from the Bessel-filte...
Figure 5.20 values of the VMG and EMG signals for four levels of contracti...
Figure 5.21 EMG value versus level of muscle contraction expressed as a pe...
Figure 5.22 VAG signal of (a) a normal subject and (b) a subject with knee-joi...
Figure 5.23 Nonparametric Parzen-window estimates of the PDFs of the VAG signa...
Figure 5.24 Nonparametric Parzen-window estimates of the PDFs of VAG signals d...
Figure 5.25 Variation of with the level of muscle contraction for the EMG si...
Chapter 6
Figure 6.1 First heart sound spectra for normal, acute myocardial infarct, a...
Figure 6.2 Averaged and normalized magnitude spectra of four patients with a...
Figure 6.3 The effect of shifting in the computation of the ACF or CCF: as t...
Figure 6.4 Commonly used window functions: rectangular, Bartlett, Hamming, a...
Figure 6.5 Log-magnitude frequency responses of the five windows illustrated...
Figure 6.6 Bartlett PSD estimate of the o2 channel of the EEG signal in Figu...
Figure 6.7 Welch PSD estimate of the o2 channel of the EEG signal in Figure ...
Figure 6.8 Top to bottom: A sample PCG signal over one cardiac cycle of a no...
Figure 6.9 Top to bottom: A sample PCG signal over one cardiac cycle of a pa...
Figure 6.10 First heart sound spectra in the case of normal and degenerated ...
Figure 6.11 Examples of signals generated based on the fBm model for differe...
Figure 6.12 (a) Example of a normal VAG signal. (b) Spectrum of the signal w...
Figure 6.13 (a) Example of an abnormal VAG signal. (b) Spectrum of the signa...
Figure 6.14 Top to bottom: Segments of the C3–A2 EEG channel of a subject du...
Figure 6.15 Top to bottom: Average PSDs of segments of the C3–A2 EEG channel...
Figure 6.16 Cluster plot of mean frequency values for EEG segments with sl...
Figure 6.17 Cluster plot of the alpha-to-slow-wave index for EEG segments ...
Figure 6.18 Cluster plot of the mean frequency and alpha-to-slow-wave index ...
Chapter 7
Figure 7.1 The train of ECG complexes in (a) is represented in terms of: (b)...
Figure 7.2 Model for production of speech, treating the vocal tract as a tim...
Figure 7.3 Synthesis of an SMUAP train and EMG interference pattern using th...
Figure 7.4 Magnitude spectra of synthesized EMG signals with (a) one motor u...
Figure 7.5 Magnitude spectra of surface EMG signals recorded from the gastro...
Figure 7.6 Normalized PSDs of synthesized point processes with (a) and , ...
Figure 7.7 Normalized PSDs of synthesized PFP trains using a real PFP wavefo...
Figure 7.8 Normalized PSDs of synthesized PFP trains using a real PFP wavefo...
Figure 7.9 IPI histograms computed from real PFP trains recorded from two no...
Figure 7.10 Normalized PSDs of the real PFP trains recorded from two normal ...
Figure 7.11 Signal-flow diagram of the AR model.
Figure 7.12 From bottom to top: Fourier spectrum; AR-model spectra with an...
Figure 7.13 Bottom to top: Fourier spectrum of S1 and the systolic portion o...
Figure 7.14 Bottom to top: Fourier spectrum of S2 and the diastolic portion ...
Figure 7.15 Bottom to top: Fourier spectrum of S1 and the systolic portion o...
Figure 7.16 Bottom to top: Fourier spectrum of S2 and the diastolic portion ...
Figure 7.17 Schematic representation of system identification. Adapted from ...
Figure 7.18 Schematic representation of system identification with separate ...
Figure 7.19 Schematic representation of system identification via iterative ...
Figure 7.20 Time-domain signals: (a) impulse response of a -pole, -zero sy...
Figure 7.22 (a) Log-magnitude spectrum of the preemphasized, real speech sig...
Figure 7.21 Log-magnitude spectra of the time-domain signals in Figure 7.20:...
Figure 7.23 (a) Mechanical and (b) electrical circuit models of parts of the...
Figure 7.24 Electromechanical model of a coronary artery tree: (a) the left ...
Figure 7.25 Hypothetical example of stenosis in coronary artery branch numbe...
Figure 7.26 Shift in frequency components predicted by the transfer-function...
Figure 7.27 Simultaneously recorded PPC signals from the upper and lower pol...
Figure 7.28 Apparatus to mimic the generation of PPC signals via a stick–sli...
Figure 7.29 Hodgkin–Huxley cell membrane circuit model. , , . and var...
Figure 7.30 Upper plot: Simulated action potential for a stimulus of and t...
Figure 7.31 The Luo–Rudy electrophysiological cell model. Luo and Rudy [56]/...
Figure 7.32 LRd model parameters under fast pacing () and slow pacing () c...
Figure 7.33 Top to bottom rows: Simulated action potentials of endocardial (...
Figure 7.34 (a) interval values from a healthy subject breathing freely. ...
Figure 7.35 (a) interval values from a healthy subject breathing at a fix...
Figure 7.36 Illustration of feature extraction based upon all-pole modeling ...
Figure 7.37 Illustration of feature extraction based upon all-pole modeling ...
Figure 7.38 Illustration of the detection of S1 and S2 via spectral tracking...
Figure 7.39 Illustration of the detection of S1 and S2 via spectral tracking...
Figure 7.40 Diastolic heart sound spectra of (a, b) two normal subjects and ...
Figure 7.41 Diastolic heart sound spectra before (preang.) and after angiopl...
Chapter 8
Figure 8.1 Contact areas of the patella with the femur during patellofemoral...
Figure 8.2 Arthroscopic views of the patellofemoral joint. (a) Normal cartil...
Figure 8.3 (a) PCG signal of a patient (female, months) with systolic murm...
Figure 8.4 Spectrogram of the PCG signal of a patient (female, months) wit...
Figure 8.5 (a) Time-domain speech signal of the word “safety” uttered by a m...
Figure 8.6 Spectrograms (log PSD) of the speech signal in Figure 8.5: (a) wi...
Figure 8.7 Adaptive segmentation of EEG signals via the use of . (a) Origin...
Figure 8.8 Elimination of transients by clipping the prediction error. (a) O...
Figure 8.9 Use of to segment an EEG signal. (a) Original EEG signal of a c...
Figure 8.10 The growing reference window, the sliding test window, and the p...
Figure 8.11 Comparative analysis of the ACF, , and GLR methods for adaptive...
Figure 8.12 Comparative analysis of the ACF, , and GLR methods for adaptive...
Figure 8.13 Comparative analysis of the ACF, , and GLR methods for adaptive...
Figure 8.14 Adaptive RLS filter for segmentation of nonstationary signals [5...
Figure 8.15 (a) Segmentation of the VAG signal of a normal subject using the...
Figure 8.16 (a) Segmentation of the VAG signal of a subject with cartilage p...
Figure 8.17 Basic unit of the lattice structure that performs the recursive ...
Figure 8.18 General schematic representation of the RLSL filter structure fo...
Figure 8.19 (a) VAG signal of a normal subject with the final segment bounda...
Figure 8.20 (a) VAG signal of a subject with cartilage pathology, with the f...
Figure 8.21 A general block diagram of the Kalman filter. Adapted from Ranga...
Figure 8.22 A. Experimental setup for BMI tasks performed by a monkey. B. Ta...
Figure 8.23 Top to bottom: The Mexican hat wavelet in Equation 8.115 for thr...
Figure 8.24 Top to bottom: The Morlet wavelet in Equation 8.116 for three sc...
Figure 8.25 Top to bottom: The Morlet wavelet in Equation 8.116 for three sc...
Figure 8.26 A noisy ECG signal over three cardiac cycles or beats (top), and...
Figure 8.27 Morlet wavelets to analyze the ECG signal in Figure 8.26. The wa...
Figure 8.28 Results of analysis of the ECG signal in Figure 8.26 using the w...
Figure 8.29 Scalogram resulting from the CWT of the ECG signal in Figure 8.2...
Figure 8.30 Normalized PSDs of the four Morlet wavelets in Figure 8.27. The ...
Figure 8.31 Examples of segmentation of EEG signals. (a) Newborn in non-REM ...
Figure 8.32 Example of detection of transients in the EEG signal of a patien...
Figure 8.33 Example of application of segmentation and pattern analysis to t...
Figure 8.34 Adaptive segmentation of the PCG signal of a normal subject usin...
Figure 8.35 Adaptive segmentation of the PCG signal of a subject (female, ...
Figure 8.36 interval series including an ischemic episode. B denotes the b...
Figure 8.37 Spectrogram of the interval series in Figure 8.36. Time progre...
Figure 8.38 Top to bottom: interval series including an ischemic episode; ...
Figure 8.39 Top to bottom: Time-varying amplitude of five cry segments; spec...
Figure 8.40 (al) Plot of a PPG signal with motion artifact. Plots of wavelet...
Figure 8.41 Illustration of SDW extracted from an organized portion of ventr...
Figure 8.42 Illustration of SDW extracted from a disorganized portion of ven...
Figure 8.43 Temporal evolution of the SDW feature for all successful (top pa...
Figure 8.44 Electrode array placement on the heart. (a) Sock array exterior ...
Figure 8.45 The leftmost plot shows the dominant frequency (DF) map computed...
Figure 8.46 The first column of the figure shows the dominant frequency (DF)...
Figure 8.47 Performance of a Kalman filter decoder for the movement of thumb...
Chapter 9
Figure 9.1 A VAG signal and IMFs (C1 to C10) obtained via EMD. The last pl...
Figure 9.2 Schematic representation of the procedure to construct a decompos...
Figure 9.3 (a) Multicomponent and nonstationary synthetic signal with a line...
Figure 9.4 Filtered version of the noisy signal in Figure 9.3 (b) using (a) ...
Figure 9.5 Filtered version of noisy signal in Figure 9.3 (b) using the matc...
Figure 9.6 (a) VAG signal of a subject with cartilage pathology and (b) filt...
Figure 9.7 Difference between the original VAG signal in Figure 9.6 (a) and ...
Figure 9.8 (a) TFD of the VAG signal in Figure 9.6 (a) and (b) the TFD of th...
Figure 9.9 (a) Multicomponent and nonstationary synthetic signal consisting ...
Figure 9.10 (a) The signal in Figure 9.9 (a) with random noise added such th...
Figure 9.11 MPTFD of the noisy signal in Figure 9.10 (a) [39].
Figure 9.12 Illustration of PCA of correlated EMG signals. (a) and (b) Two c...
Figure 9.13 Eight channels of maternal ECG signals using abdominal and thora...
Figure 9.14 Results of BSS showing eight independent components. The fifth a...
Figure 9.15 (a) A synthetic signal with three components; from left to right...
Figure 9.16 Each component in the synthetic signal is defined with four para...
Figure 9.17 Localization performance of NMF, fast ICA (FICA), and robust PCA...
Figure 9.18 Top to bottom: TF features computed using PCA, ICA, and NMF for ...
Figure 9.19 Comparison of NMF convergence with MPTFD seeding and seeding wit...
Figure 9.20 Examples of seizure and nonseizure recordings: (a) a 9 long se...
Figure 9.21 Examples of trained dictionary atoms from (a) EMD and (b) DWT-ba...
Figure 9.22 (a) VAG signal of a normal subject. A click and grinding sounds ...
Figure 9.23 (a) VAG signal of a subject with knee-joint pathology. Grinding ...
Figure 9.24 (a) EP and (b) ESP obtained from the OMPTFD of the normal VAG si...
Figure 9.25 (a) FP and (b) FSP obtained from the OMPTFD of the normal VAG si...
Figure 9.26 (a) EP and (b) ESP obtained from the OMPTFD of the abnormal VAG ...
Figure 9.27 (a) FP and (b) FSP obtained from the OMPTFD of the abnormal VAG ...
Figure 9.28 Left: TWA pattern illustration with alternate A and B patterns. ...
Figure 9.29 The NMF-adaptive spectral method for TWA detection. Adapted from...
Figure 9.30 Consecutive T waves in an ECG signal extracted and aligned to fo...
Figure 9.31 Different aspects of the NMF adaptive spectral method are shown....
Figure 9.32 ROC curves for the SM, MMA, and NMFASM (adaptive SM). Ambulatory...
Figure 9.33 Illustration of the spectral overlap between fetal and maternal ...
Figure 9.34 Block diagram of the NMF-based method for separation of maternal...
Figure 9.35 (a) Maternal and fetal activations after normalization. (b) Mate...
Figure 9.36 (a) First of the “r01” original abdominal ECG signal from the ...
Figure 9.37 EEG electrode positions used for BCI with the relevant electro...
Figure 9.38 Visual cue and timeline of the Gurve et al. [122] protocol. MI: ...
Figure 9.39 Experimental setup for motor imagery data collection. Reproduced...
Figure 9.40 The cue sequences and timeline used to guide the subjects for Da...
Figure 9.41 Examples of the original and reduced covariance matrices via NMF...
Figure 9.42 Topographic maps of EEG channel weights for subjects. Channels...
Chapter 10
Figure 10.1 Two-dimensional feature vectors of two classes and . The prot...
Figure 10.2 Illustration of a classifier based upon FLDA.
Figure 10.3 Illustration of classification of a dataset of VAG signals usi...
Figure 10.4 Schematic representation of the formulation of an SVM with two l...
Figure 10.5 A two-layer perceptron.
Figure 10.6 Schematic representation of an RBF network. The inputs to the ne...
Figure 10.7 State-conditional PDFs of a diagnostic decision variable for n...
Figure 10.8 Examples of receiver operating characteristic curves.
Figure 10.9 The -distribution curve. Reproduced with permission from T.M. C...
Figure 10.10 The ECG signal of a patient (male, years) with PVCs (training...
Figure 10.11 (a) The feature-vector space corresponding to the ECG in Figu...
Figure 10.12 The ECG signal of a patient with PVCs (test set); this portion ...
Figure 10.13 Conditional and posterior probability functions estimated for t...
Figure 10.14 Conditional and posterior probability functions estimated for t...
Figure 10.15 2D scatter plot of the feature vectors for (a) the training s...
Figure 10.16 2D scatter plot of the feature vectors for the training set o...
Figure 10.17 Categorization of knee joints based on auscultation and arthros...
Figure 10.18 A two-step classification method for the diagnosis of cartilage...
Figure 10.19 SPWVD of (a) a normal HRV signal and (b) an abnormal HRV signal...
Figure 10.20 Various symptoms of Parkinson’s disease. Reproduced with permis...
Figure 10.21 Examples of speech signals and the corresponding spectrograms f...
Figure 10.22 Clustering of NMF features of normal and pathological speech. A...
Figure 10.23 Tremor signal acquisition using triaxial inertial sensors place...
Figure 10.24 Multimodal tremor signals obtained using IMU sensors and a puls...
Figure 10.25 Pairwise feature plots of tremor signals associated with postur...
Guide
Cover
Table of Contents
Title Page
Copyright
DEDICATION
ABOUT THE AUTHOR
FOREWORD BY PROF. WILLIS J. TOMPKINS
FOREWORD BY PROF. ALAN V. OPPENHEIM
PREFACE
ACKNOWLEDGMENT
SYMBOLS AND ABBREVIATIONS
ABOUT THE COMPANION WEBSITE
Begin Reading
Index
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor-in-Chief
Moeness AminJón Atli BenediktssonAdam DrobotJames Duncan
Ekram HossainBrian JohnsonHai LiJames LykeJoydeep Mitra
Desineni Subbaram NaiduTony Q. S. QuekBehzad RazaviThomas RobertazziDiomidis Spinellis
Biomedical Signal Analysis
Third Edition
Rangaraj M. Rangayyan
University of Calgary
Calgary, AB
Canada
Sridhar Krishnan
Toronto Metropolitan University
Toronto, ON
Canada
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Cover Design: Wiley CoverImage: © Sri Krishnan
DEDICATION
Mátr dévô bhava
Pitr dévô bhava
Áchárya dévô bhava
Look upon your mother as your God
Look upon your father as your God
Look upon your teacher as your God
— from the sacred Vedic hymns of the Taittireeya Upanishad of India.
This book is dedicated to the fond memory ofmy mother Srimati Padma Srinivasan Rangayyanand my father Sri Srinivasan Mandayam Rangayyan,and to all of my teachers,in particular, Professor Ivaturi Surya Narayana Murthy.Rangaraj
This book is dedicated tomy parents, mentors, students,and my wife Mahitha,and to our children Sibi and Sarvi.Sridhar
ABOUT THE AUTHORS
Photo credit: Skogen Photography for the University of Calgary.
Rangaraj M. Rangayyan is Professor Emeritus of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada. He received the Bachelor of Engineering degree in Electronics and Communication Engineering in 1976 from the University of Mysore at the People’s Education Society College of Engineering, Mandya, Karnataka, India, and the Ph.D. in Electrical Engineering from the Indian Institute of Science, Bangalore, Karnataka, India, in 1980. He served the University of Manitoba, Winnipeg, Manitoba, Canada and the University of Calgary in research, academic, and administrative positions from 1981 to 2016. His research interests are in digital signal and image processing, biomedical signal and image analysis, and computer-aided diagnosis.
Dr. Rangayyan has published more than 170 papers in journals and 270 papers in proceedings of conferences. According to Google Scholar, Dr. Rangayyan’s publications have attracted 18,000 citations with an h-index 66. He has supervised or cosupervised 27 Master’s theses, 17 Doctoral theses, and more than 50 researchers at various levels. He has been recognized with the 1997 and 2001 Research Excellence Awards of the Department of Electrical and Computer Engineering, the 1997 Research Award of the Faculty of Engineering, appointment as “University Professor” (2003 to 2013) at the University of Calgary, and Outstanding Teaching Performance Award of the Schulich School of Engineering (2016). He is the author of two textbooks: “Biomedical Signal Analysis” (IEEE/Wiley, 2002, 2015) and “Biomedical Image Analysis” (CRC, 2005). He has coauthored and coedited several books, including “Color Image Processing with Biomedical Applications” (SPIE, 2011). He was recognized with the 2013 IEEE Canada Outstanding Engineer Medal, the IEEE Third Millennium Medal (2000), and elected as Fellow, IEEE (2001); Fellow, Engineering Institute of Canada (2002); Fellow, American Institute for Medical and Biological Engineering (2003); Fellow, SPIE (2003); Fellow, Society for Imaging Informatics in Medicine (2007); Fellow, Canadian Medical and Biological Engineering Society (2007); Fellow, Canadian Academy of Engineering (2009); and Fellow, Royal Society of Canada (2016).
Dr. Rangayyan’s research has been featured in many newsletters, magazines, and newspapers, as well as in several radio and television interviews. He has been invited to present lectures in more than 20 countries and has held Visiting or Honorary Professorships with the University of Liverpool, Liverpool, UK; Tampere University of Technology, Tampere, Finland; Universitatea Politehnica Bucureşti, Bucharest, Romania; Universidade de São Paulo, São Paulo, Brasil; Universidade Estadual Paulista, Sorocaba, São Paulo, Brasil; Cleveland Clinic Foundation, Cleveland, OH, USA; Indian Institute of Science, Bangalore, Karnataka, India; Indian Institute of Technology, Kharagpur, West Bengal, India; Manipal Institute of Technology, Manipal, Karnataka, India; Amity University, Noida, India; Beijing University of Posts and Telecommunications, Beijing, China; Xiamen University, Xiamen, Fujian, China; Kyushu University, Fukuoka, Japan; University of Rome Tor Vergata, Rome, Italy; and École Nationale Supérieure des Télécommunications de Bretagne, Brest, France. He has been recognized as a Distinguished Lecturer by the IEEE Engineering in Medicine and Biology Society (EMBS), the University of Toronto, and the Hong Kong Institution of Engineers.
For further details, please visit his website https://rangayyan.ca
Sridhar Krishnan received the B.E. degree in Electronics and Communication Engineering from the College of Engineering, Guindy, Anna University, India, in 1993, and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Calgary, Calgary, Alberta, Canada, in 1996 and 1999, respectively. He joined the Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University — TMU (formerly Ryerson University), Toronto, Ontario, Canada, in July 1999, and currently, he is a Professor in the Department. He was TMU’s Founding Program Director of the Undergraduate Biomedical Engineering Program, and also the Founding Co-Director of the Institute for Biomedical Engineering, Science and Technology (iBEST). Dr. Krishnan is an Affiliate Scientist with the University Health Network and the Keenan Research Centre, St. Michael’s Hospital, Toronto. He held the Canada Research Chair position (2007–2017) in Biomedical Signal Analysis. He has published 405 papers in refereed journals and conference proceedings, and filed/obtained 16 patents/invention disclosures. He is currently serving as a scientific advisor to six technological start-ups in the areas of digital health, wearables, and AI.
Dr. Krishnan is a recipient of the Outstanding Canadian Biomedical Engineer Award from the Canadian Medical and Biological Engineering Society, Achievement in Innovation Award from Innovate Calgary, Sarwan Sahota Distinguished Scholar Award from TMU, Young Engineer Achievement Award from Engineers Canada, New Pioneers Award in Science and Technology from Skills for Change, and Exemplary Service Award from IEEE Toronto Section. He is a Fellow of the Canadian Academy of Engineering and a registered professional engineer in the Province of Ontario.
For further details, please visit his website
https://www.ecb.torontomu.ca/people/Krishnan.html
FOREWORD BY PROF. WILLIS J. TOMPKINS
I have known Raj Rangayyan for more than 30 years. Our research and teaching careers in our respective universities both focused on the acquisition and analysis of signals from the human body. In 1993, I published a textbook called “Biomedical Digital Signal Processing” that I had developed to support the courses that I taught. Subsequently, about a decade later, in 2002, Raj published the first edition of his seminal book, “Biomedical Signal Analysis.”
In my book, I had focused mostly on the analysis of a single physiological signal, the electrocardiogram. However, the human body produces a myriad of signals, not just electrical but also thermal, acoustical, pressure, vibratory, and others. In his first edition, Raj summarized time and frequency domain tools to analyze many human biological signals from basic action potentials in myocytes to the diversity of signals produced by the physiological subsystems of the human body.
