Robust Signal Processing With Biomedical Applications
This lecture covers fundamental topics and recent developments in robust signal processing. Unlike classical signal processing, which relies strongly on the normal (Gaussian) distribution, robust methods can tolerate impulsive noise, outliers and artifacts that are frequently encountered in biomedical applications. Robust signal processing and biomedical application lectures alternate. Exercises revise the theory and apply robust signal processing algorithms to real world data.
The contens of the lecture include:
Robust Signal Processing and Learning
- Measuring robustness
- Robust estimation of the mean and the variance
- Robust regression models
- Robust filtering
- Robust location and covariance estimation
- Robust clustering and classification
- Robust time-series and spectral analysis
- Body-worn sensing of physiological parameters
- Electrocardiogram (ECG)
- Photoplethysmogram (PPG)
- Eye research
- Intracranial Pressure (ICP)
- Algorithms for cardiac activity monitoring
|Lecture/Tutorial Time and Room||
Wednesdays, 1.30-3.10 p.m. (S3|06 053)|
Thursdays, 1.30-3.10 p.m. (S3|06 053)
|Office Hours||Michael Muma: Tuesdays 9-12 a.m. (S3|06 264)|
|Prerequisites||Fundamental knowledge of statistical signal processing (Digital Signal Processing)|
|Format||Lecture and Tutorial (L3+T1); 6 credit points|
- Zoubir, A. M. and Koivunen, V. and Ollila, E. and Muma, M.: Robust Statistics for Signal Processing. Cambridge University Press, 2018.
- Zoubir, A. M. and Koivunen, V. and Chackchoukh J, and Muma, M. Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts. IEEE Signal Proc. Mag. Vol. 29, No. 4, 2012, pp. 61-80.
- Huber, P. J. and Ronchetti, E. M.: Robust Statistics. Wiley Series in Probability and Statistics, 2009.
- Maronna, R. A. and Martin, R. D. and Yohai, V. J.: Robust Statistics: Theory and Methods. Wiley Series in Probability and Statistics, 2006.