Online EEG seizure detection and localization

Amirsalar Mansouri, Sanjay P. Singh, Khalid Sayood

Research output: Contribution to journalArticle

Abstract

Epilepsy is one of the three most prevalent neurological disorders. A significant proportion of patients suffering from epilepsy can be effectively treated if their seizures are detected in a timely manner. However, detection of most seizures requires the attention of trained neurologists-a scarce resource. Therefore, there is a need for an automatic seizure detection capability. A tunable non-patient-specific, non-seizure-specific method is proposed to detect the presence and locality of a seizure using electroencephalography (EEG) signals. This multifaceted computational approach is based on a network model of the brain and a distance metric based on the spectral profiles of EEG signals. This computationally time-efficient and cost-effective automated epileptic seizure detection algorithm has a median latency of 8 s, a median sensitivity of 83%, and a median false alarm rate of 2.9%. Hence, it is capable of being used in portable EEG devices to aid in the process of detecting and monitoring epileptic patients.

Original languageEnglish (US)
Article number176
JournalAlgorithms
Volume12
Issue number9
DOIs
Publication statusPublished - Sep 1 2019

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Keywords

  • EEG
  • Epilepsy
  • Network analysis
  • Non-patient-specific
  • On-line detection
  • PSD
  • Seizure

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Numerical Analysis
  • Computational Theory and Mathematics
  • Computational Mathematics

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