Introduction of advanced biomedical signal processing methods adapted to high demands in practice, considering the limitations of signal processing methods and ways of overcoming them, learning about time-frequency analysis methods and multiresolution analysis with applications to one-dimensional signals

Analysis of correlated processes and specific processing methods; power spectral density estimation; adjustment of processing methods for the analysis of non-stationary signals, types of time-frequency analysis, wavelets transformation, feature selection principles and relevant classification methods in diagnostic decision making

Analysis of coupled and correlated physiological processes, examples of coupled processes and interactions between systems - Signal characterization in the frequency domain: estimation of power spectral density (PSD) (parametric and non-parametric methods, the use of window functions, resolution and spectral leakage), measures that can be derived from the spectral density: relation of power, moments. Illustrative examples of application of the methods in the frequency domain - Specific analysis of non-stationary signal illustration of the examples of non-stationary biomedical signals, the use of time-frekvecnisjkih methods and specific, signal segmentation for further analysis, adaptive filters - Time-frequency methods, Multiresolution analysis, wavelets transform and discrete filter banks, the application of one-dimensional biomedical signals - The application of pattern recognition in the diagnostic decision-making, application examples and unsupervised classification methods, selecting relevant features with respect to the physiological background, the measures of diagnostics accuracy and reliability of the classifier

Lectures, lab excersices