Practical Biomedical Signal Analysis Using MATLAB® 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.
The first several chapters of the text describe signal analysis techniques—including the newest and most advanced methods—in an easy and accessible way. MATLAB routines are listed when available and freely available software is discussed where appropriate. The final chapter explores the application of the methods to a broad range of biomedical signals, highlighting problems encountered in practice.
A unified overview of the field, this book explains how to properly use signal processing techniques for biomedical applications and avoid misinterpretations and pitfalls. It helps readers to choose the appropriate method as well as design their own methods.
Table of Contents
Stochastic and deterministic signals, concepts of stationarity and ergodicity
Linear time invariant systems
Duality of time and frequency domain
Surrogate data techniques
Single Channel (Univariate) Signal
Non-linear methods of signal analysis
Multiple Channels (Multivariate) Signals
Cross-estimators: cross-correlation, cross-spectra, coherence (ordinary, partial, multiple)
Multivariate autoregressive model (MVAR)
Measures of directedness
Non-linear estimators of dependencies between signals
Comparison of the multichannel estimators of coupling between time series
Multivariate signal decompositions
Application to Biomedical Signals
Brain signals: local field potentials (LFP), electrocorticogram (ECoG), electroencephalogram (EEG), and magnetoencephalogram (MEG), event related responses (ERP), and evoked fields (EF)
K.J. Blinowska is a professor at University of Warsaw, where she was director of Graduate Studies in Biomedical Physics and head of the Department of Biomedical Physics. She has been at the forefront in the development of new advanced time-series methods for research and clinical applications.
J. Żygierewicz is an assistant professor at University of Warsaw. His research focuses on time-frequency analysis of EEG and MEG signals, statistical analysis of event-related synchronization and desynchronization in EEG and MEG, and realistic neuronal network models that provide insight into the mechanisms underlying the effects observed in EEG and MEG signals.