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BY 4.0 license Open Access Published by De Gruyter September 18, 2019

Evaluation of machine learning methods for seizure prediction in epilepsy

  • Matthias Eberlein EMAIL logo , Jens Müller , Hongliu Yang , Simon Walz , Janina Schreiber , Ronald Tetzlaff , Susanne Creutz , Ortrud Uckermann and Georg Leonhardt


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.

Published Online: 2019-09-18
Published in Print: 2019-09-01

© 2019 by Walter de Gruyter Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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