Artificial Intelligence in Medicine

AI for Medical Time Series

Mittwoch, 18.03.2026

As time-series data are increasingly collected in medical environments, there is a growing need for methods to process and analyse such data. Medical time series may cover different temporal scales, ranging from milliseconds (e.g. EEG), to minutes and days (e.g. ECG or actigraphy), and up to months or years (e.g. longitudinal monitoring data). This course provides an overview of approaches used to analyse medical time-series data, including signal preprocessing, feature extraction, dimensionality reduction, and modeling concepts. Both feature-based and distance-based methods are introduced, together with basic ideas of supervised and unsupervised learning in a medical context. Topics covered include time-series representations, sampling and preprocessing, time–frequency analysis, feature extraction, dimensionality reduction, and evaluation concepts for medical time-series analysis. The course includes exercise sessions and a mini-project to provide practical experience with the discussed methods and their application to biomedical data.

Dozierende(r):	Prof. Lilian Witthauer
18.03.2026:	14:15 - 16:00
Ort:	115 Hauptgebäude Hochschulstrasse 4

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