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.