Evaluation of machine learning methods for seizure prediction in epilepsy

Abstract: Epilepsy affects about 50 million people worldwide of which one third is refractory to medication. An automated and reliable system that warns of impending seizures would greatly improve patient’s quality of life by overcoming the uncertainty and helplessness due to the unpredicted events. Here we present new seizure prediction results including a performance comparison of different methods. The analysis is based on a new set of intracranial EEG data that has been recorded in our working group during presurgical evaluation. We applied two different methods for seizure prediction and evaluated their performance pseudoprospectively. The comparison of this evaluation with common statistical evaluation reveals possible reasons for overly optimistic estimations of the performance of seizure forecasting systems.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Evaluation of machine learning methods for seizure prediction in epilepsy ; volume:5 ; number:1 ; year:2019 ; pages:109-112 ; extent:4 ; Current directions in biomedical engineering ; 5, Heft 1 (2019), 109-112 (gesamt 4)

Contributors, Places and Time

• Creator: Eberlein, Matthias ; Müller, Jens ; Yang, Hongliu ; Walz, Simon ; Schreiber, Janina ; Tetzlaff, Ronald ; Creutz, Susanne ; Uckermann, Ortrud ; Leonhardt, Georg

Further information

• DOI: 10.1515/cdbme-2019-0028
• URN: urn:nbn:de:101:1-2022101214592588912988
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:20 AM CEST