Robust and Biomedical Signal Processing

This seminar covers fundamental topics and recent developments in robust signal processing applied to biomedicine. 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.

A series of 3 lectures provides the necessary background on robust signal processing and machine learning. They are followed by two lectures on selected biomedical applications, such as, body-worn sensing of physiological parameters, electrocardiogram (ECG), photoplethysmogram (PPG), eye research and biomedical image processing.

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

Course Overview

Lecture Time and Room tba (S3|06 249)
(first lecture on tba)
Office Hours Michael Muma: Tuesdays 9-12 a.m. (S3|06 264)
Prerequisites Fundamental knowledge of statistical signal processing (Digital Signal Processing)
Language English
Format Seminar course (S4). Five weeks of lectures are followed by small research tasks and student seminar presentations
Assessment Student presentations (60%) and oral exam (40%)

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.
  • 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.