Seizure Forecasting and the Preictal State in Canine Epilepsy

Yogatheesan Varatharajah, Ravishankar K. Iyer, Brent M. Berry, Gregory A. Worrell, Benjamin H. Brinkmann

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.

Original languageEnglish (US)
Article number1650046
JournalInternational Journal of Neural Systems
Issue number1
StatePublished - Feb 1 2017


  • Epilepsy
  • ambulatory EEG
  • intracranial EEG (iEEG)
  • machine learning
  • seizure forecasting

ASJC Scopus subject areas

  • Computer Networks and Communications


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