Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy

Benjamin H. Brinkmann, Edward E. Patterson, Charles Vite, Vincent M. Vasoli, Daniel Crepeau, Matt Stead, J. Jeffry Howbert, Vladimir Cherkassky, Joost B. Wagenaar, Brian Litt, Gregory A. Worrell

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long-duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (p0.05) in 5/5 dogs analyzed.

Original languageEnglish (US)
Article numbere0133900
JournalPloS one
Volume10
Issue number8
DOIs
StatePublished - Aug 4 2015

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Fingerprint Dive into the research topics of 'Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy'. Together they form a unique fingerprint.

Cite this