Adaptive Epileptic Seizure Prediction System

Leon D. Iasemidis, Deng Shan Shiau, Wanpracha Chaovalitwongse, J. Chris Sackellares, Panos M. Pardalos, Jose C. Principe, Paul R. Carney, Awadhesh Prasad, Balaji Veeramani, Konstantinos Tsakalis

Research output: Contribution to journalArticle

326 Scopus citations

Abstract

Current epileptic seizure “prediction” algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.

Original languageEnglish (US)
Pages (from-to)616-627
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume50
Issue number5
DOIs
StatePublished - May 2003

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Keywords

  • Dynamical entrainment
  • human epilepsy
  • prediction of seizures
  • short-term maximum Lyapunov exponents
  • spatiotemporal transitions

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Iasemidis, L. D., Shiau, D. S., Chaovalitwongse, W., Sackellares, J. C., Pardalos, P. M., Principe, J. C., Carney, P. R., Prasad, A., Veeramani, B., & Tsakalis, K. (2003). Adaptive Epileptic Seizure Prediction System. IEEE Transactions on Biomedical Engineering, 50(5), 616-627. https://doi.org/10.1109/TBME.2003.810689