book

Practical Biomedical Signal Analysis Using MATLAB®, 2nd Edition

Name: Practical Biomedical Signal Analysis Using MATLAB®, 2nd Edition
ISBN: 9780429775727

by Katarzyna J. Blinowska, Jarosław Żygierewicz

October 2021

Intermediate to advanced

370 pages

10h 41m

English

CRC Press

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Cover Page
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
About the Series
Preface
List of Abbreviations
1 A Short Introduction to MATLAB®
1.1 Introduction1.2 Where Is Help?1.3 Vectors and Matrixes1.4 Matrix Operations1.4.1 Algebraic Operations1.4.2 Matrix Indexing1.4.3 Logical Indexing1.4.4 Example Exercise1.5 Conditionals1.6 Loops1.7 Scripts and Functions1.8 Working with Binary Files1.8.1 Saving to and Loading from Binary Files1.8.2 Saving and Loading Signals Using .mat Files1.8.3 Exercises1.8.3.1 Unknown Data Type1.8.3.2 Unknown Number of Channels

2 Introductory Concepts
2.1 Stochastic and Deterministic Signals, Concepts of Stationarity and Ergodicity2.2 Discrete Signals2.2.1 The Sampling Theorem2.2.1.1 Aliasing2.2.2 Quantization Error2.3 Linear Time Invariant Systems2.4 Duality of Time and Frequency Domains2.4.1 Continuous Periodic Signal2.4.2 Infinite Continuous Signal2.4.3 Finite Discrete Signal2.4.4 Basic Properties of Fourier Transform2.4.5 Power Spectrum: The Plancherel Theorem and Parseval’s Theorem2.4.6 Z-Transform2.4.7 Uncertainty Principle2.5 Hypotheses Testing2.5.1 The Null and Alternative Hypothesis2.5.2 Types of Tests2.5.3 Multiple Comparisons Problem2.5.3.1 Correcting the Significance Level2.5.3.2 Parametric and Non-Parametric Statistical Maps2.5.3.3 False Discovery Rate2.6 Surrogate Data Techniques
3 Single Channel (Univariate) Signal
3.1 Filters3.1.1 Designing Filters3.1.2 Changing the Sampling Frequency3.1.3 Matched Filters3.1.4 Wiener Filter3.2 Probabilistic Models3.2.1 Hidden Markov Model3.2.2 Kalman Filters3.3 Stationary Signals3.3.1 Analytic Tools in the Time Domain3.3.1.1 Mean Value, Amplitude Distributions3.3.1.2 Entropy and Information Measure3.3.1.3 Autocorrelation Function3.3.2 Analytic Tools in the Frequency Domain3.3.2.1 Estimators of Spectral Power Density Based on Fourier Transform3.3.2.2 Choice of Windowing Function3.3.2.3 Parametric Models: AR, ARMA3.4 Non-Stationary Signals3.4.1 Instantaneous Amplitude and Instantaneous Frequency3.4.2 Analytic Tools in the Time-Frequency Domain3.4.2.1 Time-Frequency Energy Distributions3.4.2.2 Time-Frequency Signal Decompositions3.4.3 Cross-Frequency Coupling3.4.3.1 Models of Phase-Amplitude Coupling3.4.3.2 Evaluation of Phase-Amplitude Coupling3.5 Non-Linear Methods of Signal Analysis3.5.1 Lyapunov Exponent3.5.2 Correlation Dimension3.5.3 Detrended Fluctuation Analysis3.5.4 Recurrence Plots3.5.5 Poincaré Map3.5.6 Approximate, Sample, and Multiscale Entropy3.5.7 Limitations of Non-Linear Methods
4 Multiple Channels (Multivariate) Signals
4.1 Cross-Estimators: Cross-Correlation, Cross-Spectra, Coherence4.2 Multivariate Autoregressive Model (MVAR)4.2.1 Formulation of MVAR Model4.2.2 MVAR in the Frequency Domain4.3 Measures of Directedness4.3.1 Estimators Based on the Phase Difference4.3.2 Causality Measures4.3.2.1 Granger Causality4.3.2.2 Granger Causality Index and Granger-Geweke Causality4.3.2.3 Directed Transfer Function4.3.2.4 Partial Directed Coherence4.3.2.5 Directed Coherence4.4 Non-Linear Estimators of Dependencies between Signals4.4.1 Kullback-Leibler Entropy, Mutual Information4.4.2 Transfer Entropy4.4.3 Generalized Synchronization and Synchronization Likelihood4.4.4 Phase Synchronization (Phase Locking Value)4.4.5 Testing the Reliability of the Estimators of Directedness4.5 Comparison of the Multichannel Estimators of Coupling between Time Series4.5.1 Bivariate versus Multivariate Connectivity Estimators4.5.2 Linear versus Non-Linear Estimators of Connectivity4.5.3 The Measures of Directedness4.6 Multivariate Signal Decompositions4.6.1 Principal Component Analysis (PCA)4.6.1.1 Definition4.6.1.2 Computation4.6.1.3 Possible Applications4.6.2 Independent Components Analysis (ICA)4.6.2.1 Definition4.6.2.2 Estimation of ICA4.6.2.3 Computation4.6.2.4 Possible Applications4.6.3 Common Spatial Patterns4.6.4 Multivariate Matching Pursuit (MMP)
5 Application to Biomedical Signals
5.1 Brain Signals5.1.1 Generation of Brain Signals5.1.2 EEG/MEG Rhythms5.1.3 EEG Measurement, Electrode Systems5.1.4 MEG Measurement, Sensor Systems5.1.5 Elimination of Artifacts5.1.6 Analysis of Continuous EEG Signals5.1.6.1 Single Channel Analysis5.1.6.2 Mapping5.1.6.3 Connectivity Analysis of Brain Signals5.1.6.4 Influence of Volume Conduction on Connectivity Measures5.1.6.5 Graph Theoretical Analysis5.1.6.6 Sleep EEG Analysis5.1.6.7 Analysis of EEG in Epilepsy5.1.6.8 EEG in Monitoring and Anesthesia5.1.7 Analysis of Epoched EEG Signals5.1.7.1 Analysis of Phase-Locked Responses5.1.7.2 In Pursuit of Single Trial Evoked Responses5.1.7.3 Applications of Cross-Frequency Coupling5.1.7.4 Analysis of Non-Phase-Locked Responses5.1.7.5 Analysis of EEG for Applications in Brain-Computer Interfaces5.1.8 fMRI Derived Time Series5.1.8.1 Relation between EEG and fMRI5.1.9 Near-Infrared Spectroscopy Signals5.2 Heart Signals5.2.1 Electrocardiogram5.2.1.1 Measurement Standards5.2.1.2 Physiological Background and Clinical Applications5.2.1.3 Processing of ECG5.2.2 Heart Rate Variability5.2.2.1 Time-Domain Methods of HRV Analysis5.2.2.2 Frequency-Domain Methods of HRV Analysis5.2.2.3 Non-Linear Methods of HRV Analysis5.2.3 Fetal ECG5.2.4 Magnetocardiogram and Fetal Magnetocardiogram5.2.4.1 Magnetocardiogram5.2.4.2 Fetal MCG5.2.5 Ballistocardiogram, Seismocardiogram, Photoplethysmogram5.2.5.1 Wearable Devices5.3 Electromyogram5.3.1 Measurement Techniques and Physiological Background5.3.2 Quantification of EMG Features5.3.3 Decomposition of Needle EMG5.3.4 Surface EMG5.3.4.1 Surface EMG Decomposition5.4 Acoustic Signals5.4.1 Phonocardiogram5.4.2 Otoacoustic Emissions5.5 Multimodal Analysis of Biomedical Signals
Bibliography
Index

Overview

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.

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Practical Biomedical Signal Analysis Using MATLAB

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