ETI160 Biomedical Signal Processing
The function of the human body is frequently associated with signals of electrical, chemical, or acoustic origin. Such signals convey information which may not be immediately perceived but which is hidden in the signal's structure. This information has to be extracted in some way before the signals can be given meaningful interpretation. The signals reflect properties of their associated underlying biological systems, and their decoding has been found very helpful in explaining and identifying various pathological conditions. A signal's complexity is often considerable, and, therefore, biomedical signal processing has become an indispensable tool for extracting clinically significant information hidden in the signal.
The course offers an overview of signal processing techniques which are used in biomedical applications. Special emphasis is given to signals of bioelectrical origin as their analysis and interpretation are essential in many clinical applications. The characteristics of electrical signals from the brain and the heart are described as well as the characteristics of various noise sources which often interfer with these signals. Knowledge about these characteristics is crucial when designing methods which detect, e.g., changes in a subject's sleep state or the presence of micropotential activity in cardiac activity.
The analysis of biological rhythms is currently receiving much attention, in particular the study of heart rate variability which provides useful information on the autonomic nervous system activity. The course describes signal processing techniques for such analysis in the time domain as well as in the frequency domain.
Note that the course may well be followed by those who has primary interest in communications as the course illustrates the interplay between signal processing in theory and practice.
Contents: Bioelectrical signals (origin and characteristics), spectral analysis (nonparametric and parametric) and time-frequency analysis, event detection, data compression, time-synchronized noise reduction (ensemble averaging, orthogonal transforms), nonlinear filtering.