Signal Processing for Biomedical Engineering - Interactive curriculae of TU Ilmenau

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module properties Signal Processing for Biomedical Engineering in degree program Master Biomedical Engineering by Research 2026
module number	201267
examination number	2200891
department	Department of Computer Science and Automation
ID of group	2225 (Data Processing in the Life Sciences)
module leader	Prof. Dr. Patrique Fiedler
term	winter term only
language	Englisch
credit points	5
on-campus program (h)	34
self-study (h)	116
obligation	obligatory module
exam	written examination performance, 90 minutes
details of the certificate
link to Moodle course
teacher	Prof. Fiedler
signup details for alternative examinations
maximum number of participants
previous knowledge and experience	- Signal and systems theory - Math - Basics in anatomy and physiology - Basics in electro- and neurophysiology - Electrical measurement technology - Process measurement and sensor technology
learning outcome	Professional competences: The students know the most important biosignals in terms of amplitude and frequency characteristics as well as their stochastic properties. Methodological competences: Students are able to analyze and understand basic algorithms and processes for the statistical description of biosignals. Students have the competence to select the relevant approaches to solving a specific processing or analysis task from the multitude of methods available and to evaluate the possibilities and limitations of these methods. Social skills: The students are able to discuss and evaluate processing and analysis approaches and algorithms that are designed in the seminar in a team. They can clearly communicate their own arguments and thoughts and appreciate the contributions of other students.
content	Basics of statistics for analyzing stochastic processes Stationarity, ergodicity, ensemble model Power spectral density: direct and indirect methods Windowing of biosignals Periodogram: Methods according to Bartlett and Welch Estimation of correlation functions: unbiased and biased methods Cross power density and coherence Spectral estimation with parametric models, linear prediction Fourier series and transform, DFT, FFT Methods of time-frequency analysis, time variant distributions STFT and spectrogram Wavelets: theory, algorithm and practical implementation
media of instruction and technical requirements for education and examination in case of online participation	Slides with projector for the lecture, blackboard, computer simulations. Whiteboard and computing cabinet for the seminar.
literature / references	Bronzino, J. D. (Ed.): The Biomedical Engineering Handbook, Vol. I + II, 2nd ed., CRC Press, Boca Raton 2000 Husar, P.: Electrical Biosignals in Biomedical Engineering , Springer, 2023, 1st edition. Akay M.: Time-Frequency and Wavelets in Biomedical Signal Processing. IEEE Press, 1998 Bendat J., Piersol A.: Measurement and Analysis of Random Data. John Wiley, 1986. Proakis, J.G, Manolakis, D.G.: Digital Signal Processing, Pearson Prentice Hall, 2007
evaluation of teaching