A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings

Jui Hong Chien, Deng Shan Shiau, J. J. Halford, K. M. Kelly, R. T. Kern, M. C.K. Yang, Jicong Zhang, J. Ch Sackellares, P. M. Pardalos

Research output: Contribution to journalArticlepeer-review

Abstract

The purpose of this study was to evaluate a signal regularity-based automated seizure prediction algorithm for scalp EEG. Signal regularity was quantified using the Pattern Match Regularity Statistic (PMRS), a statistical measure. The primary feature of the prediction algorithm is the degree of convergence in PMRS ("PMRS entrainment") among the electrode groups determined in the algorithm training process. The hypothesis is that the PMRS entrainment increases during the transition between interictal and ictal states, and therefore may serve as an indicator for prediction of an impending seizure.

Original languageEnglish (US)
Pages (from-to)586-597
Number of pages12
JournalCybernetics and Systems Analysis
Volume47
Issue number4
DOIs
StatePublished - Jul 2011
Externally publishedYes

Keywords

  • brain dynamics
  • epileptic seizure
  • scalp electroencephalogram
  • seizure warning

ASJC Scopus subject areas

  • Computer Science(all)

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