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E-raamat: Practical Biomedical Signal Analysis Using MATLAB(R)

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Fully updated and with exclusive new content, this second edition presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data.



Covering the latest cutting-edge techniques in biomedical signal processing while presenting a coherent treatment of various signal processing methods and applications, this second edition of Practical Biomedical Signal Analysis Using MATLAB® also offers practical guidance on which procedures are appropriate for a given task and different types of data.

It begins by describing signal analysis techniques—including the newest and most advanced methods in the field —in an easy and accessible way, illustrating them with Live Script demos. MATLAB® routines are listed when available, and freely available software is discussed where appropriate. The book concludes by exploring the applications of the methods to a broad range of biomedical signals while highlighting common problems encountered in practice.

These chapters have been updated throughout and include new sections on multiple channels analysis and connectivity measures, phase-amplitude analysis, functional Near Infrared Spectroscopy, fMRI (BOLD) signals, wearable devices, multimodal signal analysis and the Brain Computer Interface.

By providing a unified overview of the field, this book explains how to integrate signal processing techniques in biomedical applications properly and explores how to avoid misinterpretations and pitfalls. It helps readers to choose the appropriate method as well as design their own methods. It will be an excellent guide for graduate students studying biomedical engineering and practicing researchers in the field of biomedical signal analysis.

Features:

  • Fully updated throughout with new achievements, technologies, and methods and is supported with over 40 original Matlab Live Scripts illustrating the discussed techniques, suitable for self-learning or as a supplement to college courses

  • Provides a practical comparison of the advantages and disadvantages of different approaches in the context of various applications

    • Applies the methods to a variety of signals, including electric, magnetic, acoustic and optical

    • K. J. Blinowska

    is a Professor emeritus at the University of Warsaw, Poland, where she was director of Graduate Studies in Biomedical Physics and head of the Department of Biomedical Physics. Currently, she is employed at the Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. She has been at the forefront in developing new advanced time-series methods for research and clinical applications.

    J. Zygierewicz

    is a Professor at the University of Warsaw, Poland. His research focuses on developing methods for analyzing EEG and MEG signals, brain-computer interfaces, applications of machine learning in signal processing and classification.

    About the Series xi
    Preface xiii
    List of Abbreviations
    		xv
    1 A Short Introduction to MATLAB®
    		1(10)
    1.1 Introduction
    		1(1)
    1.2 Where Is Help?
    		1(1)
    1.3 Vectors and Matrixes
    		1(1)
    1.4 Matrix Operations
    		2(3)
    1.4.1 Algebraic Operations
    		2(1)
    1.4.2 Matrix Indexing
    		3(1)
    1.4.3 Logical Indexing
    		4(1)
    1.4.4 Example Exercise
    		5(1)
    1.5 Conditionals
    		5(1)
    1.6 Loops
    		6(1)
    1.7 Scripts and Functions
    		6(1)
    1.8 Working with Binary Files
    		7(4)
    1.8.1 Saving to and Loading from Binary Files
    		7(2)
    1.8.2 Saving and Loading Signals Using mat Files
    		9(1)
    1.8.3 Exercises
    		9(1)
    1.8.3.1 Unknown Data Type
    		9(1)
    1.8.3.2 Unknown Number of Channels
    		10(1)
    2 Introductory Concepts
    		11(24)
    2.1 Stochastic and Deterministic Signals, Concepts of Stationarity and Ergodicity
    		11(3)
    2.2 Discrete Signals
    		14(4)
    2.2.1 The Sampling Theorem
    		14(1)
    2.2.1.1 Aliasing
    		15(2)
    2.2.2 Quantization Error
    		17(1)
    2.3 Linear Time Invariant Systems
    		18(3)
    2.4 Duality of Time and Frequency Domains
    		21(7)
    2.4.1 Continuous Periodic Signal
    		22(1)
    2.4.2 Infinite Continuous Signal
    		22(1)
    2.4.3 Finite Discrete Signal
    		23(1)
    2.4.4 Basic Properties of Fourier Transform
    		23(1)
    2.4.5 Power Spectrum: The Plancherel Theorem and Parse-val's Theorem
    		24(1)
    2.4.6 Z-Transform
    		25(1)
    2.4.7 Uncertainty Principle
    		26(2)
    2.5 Hypotheses Testing
    		28(5)
    2.5.1 The Null and Alternative Hypothesis
    		28(1)
    2.5.2 Types of Tests
    		28(1)
    2.5.3 Multiple Comparisons Problem
    		29(2)
    2.5.3.1 Correcting the Significance Level
    		31(1)
    2.5.3.2 Parametric and Non-Parametric Statistical Maps
    		31(1)
    2.5.3.3 False Discovery Rate
    		32(1)
    2.6 Surrogate Data Techniques
    		33(2)
    3 Single Channel (Univariate) Signal
    		35(68)
    3.1 Filters
    		35(8)
    3.1.1 Designing Filters
    		37(4)
    3.1.2 Changing the Sampling Frequency
    		41(1)
    3.1.3 Matched Filters
    		42(1)
    3.1.4 Wiener Filter
    		42(1)
    3.2 Probabilistic Models
    		43(5)
    3.2.1 Hidden Markov Model
    		43(2)
    3.2.2 Kalman Filters
    		45(3)
    3.3 Stationary Signals
    		48(16)
    3.3.1 Analytic Tools in the Time Domain
    		48(1)
    3.3.1.1 Mean Value, Amplitude Distributions
    		48(1)
    3.3.1.2 Entropy and Information Measure
    		48(1)
    3.3.1.3 Autocorrelation Function
    		49(1)
    3.3.2 Analytic Tools in the Frequency Domain
    		50(1)
    3.3.2.1 Estimators of Spectral Power Density Based on Fourier Transform
    		50(1)
    3.3.2.2 Choice of Windowing Function
    		51(5)
    3.3.2.3 Parametric Models: AR, ARMA
    		56(8)
    3.4 Non-Stationary Signals
    		64(29)
    3.4.1 Instantaneous Amplitude and Instantaneous Frequency
    		64(2)
    3.4.2 Analytic Tools in the Time-Frequency Domain
    		66(1)
    3.4.2.1 Time-Frequency Energy Distributions
    		66(3)
    3.4.2.2 Time-Frequency Signal Decompositions
    		69(17)
    3.4.3 Cross-Frequency Coupling
    		86(1)
    3.4.3.1 Models of Phase-Amplitude Coupling
    		87(1)
    3.4.3.2 Evaluation of Phase-Amplitude Coupling
    		88(5)
    3.5 Non-Linear Methods of Signal Analysis
    		93(10)
    3.5.1 Lyapunov Exponent
    		95(1)
    3.5.2 Correlation Dimension
    		95(2)
    3.5.3 Detrended Fluctuation Analysis
    		97(1)
    3.5.4 Recurrence Plots
    		98(1)
    3.5.5 Poincare Map
    		99(1)
    3.5.6 Approximate, Sample, and Multiscale Entropy
    		99(2)
    3.5.7 Limitations of Non-Linear Methods
    		101(2)
    4 Multiple Channels (Multivariate) Signals
    		103(34)
    4.1 Cross-Estimators: Cross-Correlation, Cross-Spectra, Coherence
    		103(3)
    4.2 Multivariate Autoregressive Model (MVAR)
    		106(2)
    4.2.1 Formulation of MVAR Model
    		106(2)
    4.2.2 MVAR in the Frequency Domain
    		108(1)
    4.3 Measures of Directedness
    		108(10)
    4.3.1 Estimators Based on the Phase Difference
    		108(1)
    4.3.2 Causality Measures
    		109(1)
    4.3.2.1 Granger Causality
    		109(1)
    4.3.2.2 Granger Causality Index and Granger-Geweke Causality
    		110(1)
    4.3.2.3 Directed Transfer Function
    		111(4)
    4.3.2.4 Partial Directed Coherence
    		115(1)
    4.3.2.5 Directed Coherence
    		116(2)
    4.4 Non-Linear Estimators of Dependencies between Signals
    		118(7)
    4.4.1 Kullback-Leibler Entropy, Mutual Information
    		118(1)
    4.4.2 Transfer Entropy
    		119(2)
    4.4.3 Generalized Synchronization and Synchronization Likelihood
    		121(1)
    4.4.4 Phase Synchronization (Phase Locking Value)
    		122(1)
    4.4.5 Testing the Reliability of the Estimators of Directedness
    		123(2)
    4.5 Comparison of the Multichannel Estimators of Coupling between Time Series
    		125(4)
    4.5.1 Bivariate versus Multivariate Connectivity Estimators
    		125(2)
    4.5.2 Linear versus Non-Linear Estimators of Connectivity
    		127(1)
    4.5.3 The Measures of Directedness
    		128(1)
    4.6 Multivariate Signal Decompositions
    		129(8)
    4.6.1 Principal Component Analysis (PCA)
    		129(1)
    4.6.1.1 Definition
    		129(1)
    4.6.1.2 Computation
    		129(1)
    4.6.1.3 Possible Applications
    		130(1)
    4.6.2 Independent Components Analysis (ICA)
    		130(1)
    4.6.2.1 Definition
    		130(1)
    4.6.2.2 Estimation of ICA
    		131(1)
    4.6.2.3 Computation
    		132(1)
    4.6.2.4 Possible Applications
    		132(1)
    4.6.3 Common Spatial Patterns
    		133(1)
    4.6.4 Multivariate Matching Pursuit (MMP)
    		134(3)
    5 Application to Biomedical Signals
    		137(152)
    5.1 Brain Signals
    		137(94)
    5.1.1 Generation of Brain Signals
    		139(1)
    5.1.2 EEG/MEG Rhythms
    		140(3)
    5.1.3 EEG Measurement, Electrode Systems
    		143(2)
    5.1.4 MEG Measurement, Sensor Systems
    		145(1)
    5.1.5 Elimination of Artifacts
    		145(4)
    5.1.6 Analysis of Continuous EEG Signals
    		149(1)
    5.1.6.1 Single Channel Analysis
    		150(1)
    5.1.6.2 Mapping
    		151(1)
    5.1.6.3 Connectivity Analysis of Brain Signals
    		152(4)
    5.1.6.4 Influence of Volume Conduction on Connectivity Measures
    		156(1)
    5.1.6.5 Graph Theoretical Analysis
    		157(3)
    5.1.6.6 Sleep EEG Analysis
    		160(9)
    5.1.6.7 Analysis of EEG in Epilepsy
    		169(9)
    5.1.6.8 EEG in Monitoring and Anesthesia
    		178(1)
    5.1.7 Analysis of Epoched EEG Signals
    		179(3)
    5.1.7.1 Analysis of Phase-Locked Responses
    		182(7)
    5.1.7.2 In Pursuit of Single Trial Evoked Responses
    		189(5)
    5.1.7.3 Applications of Cross-Frequency Coupling
    		194(2)
    5.1.7.4 Analysis of Non-Phase-Locked Responses
    		196(19)
    5.1.7.5 Analysis of EEG for Applications in Brain-Computer Interfaces
    		215(3)
    5.1.8 fMRI Derived Time Series
    		218(3)
    5.1.8.1 Relation between EEG and fMRI
    		221(4)
    5.1.9 Near-Infrared Spectroscopy Signals
    		225(6)
    5.2 Heart Signals
    		231(30)
    5.2.1 Electrocardiogram
    		231(1)
    5.2.1.1 Measurement Standards
    		231(1)
    5.2.1.2 Physiological Background and Clinical Applications
    		232(3)
    5.2.1.3 Processing of ECG
    		235(6)
    5.2.2 Heart Rate Variability
    		241(1)
    5.2.2.1 Time-Domain Methods of HRV Analysis
    		242(1)
    5.2.2.2 Frequency-Domain Methods of HRV Analysis
    		243(1)
    5.2.2.3 Non-Linear Methods of HRV Analysis
    		244(7)
    5.2.3 Fetal ECG
    		251(2)
    5.2.4 Magnetocardiogram and Fetal Magnetocardiogram
    		253(1)
    5.2.4.1 Magnetocardiogram
    		253(4)
    5.2.4.2 Fetal MCG
    		257(1)
    5.2.5 Ballistocardiogram, Seismocardiogram, Photoplethys-mogram
    		258(1)
    5.2.5.1 Wearable Devices
    		259(2)
    5.3 Electromyogram
    		261(17)
    5.3.1 Measurement Techniques and Physiological Background
    		261(4)
    5.3.2 Quantification of EMG Features
    		265(1)
    5.3.3 Decomposition of Needle EMG
    		266(3)
    5.3.4 Surface EMG
    		269(1)
    5.3.4.1 Surface EMG Decomposition
    		270(8)
    5.4 Acoustic Signals
    		278(8)
    5.4.1 Phonocardiogram
    		278(3)
    5.4.2 Otoacoustic Emissions
    		281(5)
    5.5 Multimodal Analysis of Biomedical Signals
    		286(3)
    Bibliography 289(60)
    Index 349
    K. J. Blinowskais a Professor emeritus at the University of Warsaw, Poland, where she was director of Graduate Studies in Biomedical Physics and head of the Department of Biomedical Physics. Currently, she is employed at the Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. She has been at the forefront in developing new advanced time-series methods for research and clinical applications.









    J. ygierewiczis a Professor at the University of Warsaw, Poland. His research focuses on developing methods for analyzing EEG and MEG signals, brain-computer interfaces, applications of machine learning in signal processing and classification.
    Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
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