Papers › Dimension reduction methods, persistent homology and machine learning for EEG signal...
Dimension reduction methods, persistent homology and machine learning for EEG signal analysis of Interictal Epileptic Discharges
Annika Stiehl, Stefan Geißelsöder, Nicole Ille, Fabienne Anselstetter, Harald Bornfleth, Christian Uhl
Recognizing specific events in medical data requires trained personnel. To aid the classification, machine learning algorithms can be applied. In this context, medical records are usually high-dimensional, although a lower dimension can also reflect the dynamics of the signal. In this study, electroencephalogram data with Interictal Epileptic Discharges (IEDs) are investigated. First, the dimensions are reduced using Dynamical Component Analysis (DyCA) and Principal Component Analysis (PCA), respectively. The reduced data are examined using topological data analysis (TDA), specifically using a persistent homology algorithm. The persistent homology results are used for targeted feature generation. The features are used to train and evaluate a Support Vector Machine (SVM) to distinguish IEDs from background activities.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections