Signal regularity-based automated seizure detection system for scalp EEG monitoring 1

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

Research output: Contribution to journalArticlepeer-review

Abstract

The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multichannel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset.

Original languageEnglish (US)
Pages (from-to)922-935
Number of pages14
JournalCybernetics and Systems Analysis
Volume46
Issue number6
DOIs
StatePublished - Nov 2010
Externally publishedYes

Keywords

  • Amplitude variation
  • Artifact rejection
  • False detection rate
  • Local maximum frequency
  • Pattern match regularity statistic (PMRS)
  • Scalp EEG
  • Seizure detection
  • Sensitivity

ASJC Scopus subject areas

  • Computer Science(all)

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