Practical Biomedical Signal Analysis Using MATLAB®

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.

Introductory Concepts
Stochastic and deterministic signals, concepts of stationarity and ergodicity
Discrete signals
Linear time invariant systems
Duality of time and frequency domain
Hypotheses testing
Surrogate data techniques


Single Channel (Univariate) Signal
Filters
Probabilistic models
Stationary signals
Non-stationary signals
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)
Heart signals
Electromyogram
Gastro-intestinal signals 
Acoustic signals


References


Index