Biomedical Signal Processing

Sources and types of biomedical signals, goals of signal processing. Random variable, probability functions, functions of random variables. Random processes, moment functions. Correlation, convolution, coherence. Parameter estimation based on time-limited random signals. Stationarity and nonstationarity of random signals, assessment of stationarity. Power spectral density and its estimates based on classical (Fourier-based) and modern approaches (based on parametric modeling of random signals). Data windows. Parametric modeling of random processes and linear prediction. Common electrophysiological signals, their properties and common signal processing approaches (EKG, EMG, EEG). Noise in biomedical signals and filtering. Optimal and adaptive filtering. Event and wavelet detection. Cepstrum and homomorphic deconvolution. Time-frequency analysis of non-stationary signals (using short-time Fourier transform and wavelet transform).

To get insight into principles of random processes in relation to signal processing applications. Understanding of theoretical background of various methods for biomedical signal processing and to recognize practical usefulness of these methods for extraction of information from common electrophysiological and other signals of biomedical origin.

Lectures, individual practical lab work, self study. One part of lab work can be replaced by project work (individual or team assignment). Practical work involves application of methods for signal processing on real signals of biomedical origin (signals from clinical environment or students' own signals recorded during lab assignments from other courses).