Robust Signal Processing With Biomedical Applications

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 contents 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

Biomedical Applications

  • Body-worn sensing of physiological parameters
  • Electrocardiogram (ECG)
  • Photoplethysmogram (PPG)
  • Eye research
  • Intracranial Pressure (ICP)
  • Algorithms for cardiac activity monitoring

This course is maintained via moodle. Please register via TUCaN to gain access to the moodle course.

Course Overview

Lecture/Tutorial Time and Room Wednesdays, 1.30-3.10 p.m. (online)
Thursdays, 1.30-3.10 p.m. (online)
(first lecture on April 23, 2020)
Office Hours Michael Muma: contact me via email
Prerequisites Fundamental knowledge of statistical signal processing (Digital Signal Processing)
Language English
Format Lecture and Tutorial (L3+T1); 6 credit points
Assessment Written exam

Literature

  • 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.

Toolboxes

Matlab, Python and R Toolboxes that implement the algorithms that are treated in the lecture are available here:

https://github.com/RobustSP/