Biomedical Signal Processing

Subject description

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).

The subject is taught in programs

Objectives and competences

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.

Teaching and learning methods

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).

Expected study results

After successful completion of the course, students shoud be able to:

– Expand knowledge about signal processing approaches from deterministic to random signals.

– Classify signals according to their general properties.

– Present the most commonly encountered signals of biomedical origin and application areas for these signals.

– Apply various mathematical tools (using MATLAB) for extraction of clinically relevant information from biomedical and other stochastic signals.

– Apply mathematical tools for analysis of nonstationary signals in the time-frequency domain.

– Select and analyse additional literature to solve a concrete specific problem either independently or as member of a group.

– Design a program to solve a specific problem.

Basic sources and literature

  1. E.N. Bruce: Biomedical signal processing and signal modeling. Wiley-Interscience, 200
  2. R.M. Rangayyan: Biomedical signal analysis: a case-study approach. Wiley-IEEE Press, 2001.
  3. L. Soernmo, P. Laguna: Bioelectrical signal processing in cardiac and neurological applications. Academic Press, 2005.
  4. H. Stark, J.W. Woods: Probability and random processes with applications to signal processing (3rd ed.). Prentice Hall, 2002.
  5. J.L. Semmlow: Biosignal and biomedical image processing: MATLAB-based applications. CRC Press, 2004.
  6. T. Jarm, S. Reberšek: Obdelava biomedicinskih signalov. Založba FE in FRI, 2005.

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