Quickest detection of drug-resistant seizures: An optimal control approach

Sabato Santaniello, Samuel P. Burns, Alexandra J. Golby, Jedediah M. Singer, William S. Anderson, Sridevi V. Sarma

Research output: Contribution to journalArticlepeer-review

Abstract

Epilepsy affects 50 million people worldwide, and seizures in 30% of the cases remain drug resistant. This has increased interest in responsive neurostimulation, which is most effective when administered during seizure onset. We propose a novel framework for seizure onset detection that involves (i) constructing statistics from multichannel intracranial EEG (iEEG) to distinguish nonictal versus ictal states; (ii) modeling the dynamics of these statistics in each state and the state transitions; you can remove this word if there is no room. (iii) developing an optimal control-based "quickest detection" (QD) strategy to estimate the transition times from nonictal to ictal states from sequential iEEG measurements. The QD strategy minimizes a cost function of detection delay and false positive probability. The solution is a threshold that non-monotonically decreases over time and avoids responding to rare events that normally trigger false positives. We applied QD to four drug resistant epileptic patients (168 hour continuous recordings, 26-44 electrodes, 33 seizures) and achieved 100% sensitivity with low false positive rates (0.16 false positive/hour). This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Original languageEnglish (US)
Pages (from-to)S49-S60
JournalEpilepsy and Behavior
Volume22
Issue numberSUPPL. 1
DOIs
StatePublished - Dec 2011

Keywords

  • Bayesian estimation
  • Dynamic programming
  • Hidden Markov model
  • Intracranial electroencephalogram
  • Multivariate analysis
  • Networks
  • Optimal control
  • Quickest detection

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Behavioral Neuroscience

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