Reliable seizure prediction from EEG data

Vladimir Cherkassky, Brandon Veber, Jieun Lee, Han Tai Shiao, Ned Patterson, Gregory Alan Worrell, Benjamin Brinkmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

There is a growing interest in data-analytic modeling for prediction and/or detection of epileptic seizures from EEG recording of brain activity [1-10]. Even though there is clear evidence that many patients have changes in EEG signal prior to seizures, development of robust seizure prediction methods remains elusive [1]. We argue that the main issue for development of effective EEG-based predictive models is an apparent disconnect between clinical considerations and dataanalytic modeling assumptions. We present an SVM-based system for seizure prediction, where design choices and performance metrics are clearly related to clinical objectives and constraints. This system achieves very accurate prediction of preictal and interictal EEG segments in dogs with naturally occurring epilepsy. However, our empirical results suggest that good prediction performance may be possible only if the training data set has sufficiently many preictal segments, i.e. at least 6-7 seizure episodes.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2015-September
ISBN (Print)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs
StatePublished - Sep 28 2015
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: Jul 12 2015Jul 17 2015

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2015
Country/TerritoryIreland
CityKillarney
Period7/12/157/17/15

Keywords

  • epilepsy
  • iEEG
  • patient-specific modeling
  • predictive data analytics
  • seizure prediction
  • SVM classification
  • unbalanced data

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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