Convolutional neural networks for epileptic seizure prediction

Matthias Eberlein, Raphael Hildebrand, Ronald Tetzlaff, Nico Hoffmann, Levin Kuhlmann, Benjamin Brinkmann, Jens Müller

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

30 Citations (Scopus)


Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine
Subtitle of host publication3-6 Dec. 2018, Madrid, Spain
EditorsHuiru (Jane) Zheng, Xiaohua Hu, Zoraida Callejas, Harald Schmidt, David Griol, Jan Baumbach, Haiying Wang, Julie Dickerson, Le Zhang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538654880, 9781538654873
ISBN (Print)9781538654897
Publication statusPublished - 2018
EventIEEE International Conference on Bioinformatics and Biomedicine 2018 - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018


ConferenceIEEE International Conference on Bioinformatics and Biomedicine 2018
Abbreviated titleBIBM 2018
Internet address

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