Biomedical signal processing

Higher education teachers: Jarm Tomaž

Subject description

Prerequisits:

Inscription in the 2nd cycle (masters level) of the study. College-level knowledge in mathematics, system theory and signal processing is required (for example, previously taken courses Mathematics 1 through Mathematics 4 and Signals from the 1st cycle). Positive grades from practical work and the final exam are required to complete the course.

Content (Syllabus outline):

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).

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.

Intended learning outcomes:

Knowledge and understanding: To expand knowledge about signal processing approaches from deterministic to random signals; to learn about typical biomedical signals; to gain theoretical understanding of mathematical methods used in processing of such signals.

Practical use: Application of signal processing methods to solve problems related to extraction of clinically relevant information from medical signals (using Matlab); to be able to select and justify an appropriate method based on the purpose of signal processing.

Refelxion and transferable skills: Ability to investigate the problem at hand individually or as a member of a team, to search for new sources of information, to be able to evaluate critically the results of oneself or others.

Learning and teaching 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).

Study materials

Readings:

1. E.N. Bruce: Biomedical signal processing and signal modeling. Wiley-Interscience, 2001.
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.