EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

Negar Ahmadi (Corresponding author), Yulong Pei, Evelien Carrette, Albert P. Aldenkamp, Mykola Pechenizkiy

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of an epileptic seizure the brain will show epileptiform discharges on the electroencephalogram (EEG). In many cases an accurate diagnosis can only be achieved after a long-term video monitoring combined with EEG recording which is quite expensive and time-consuming. In this paper using short-term EEG data, the classification of epilepsy and PNES subjects is analyzed based on signal, functional network and EEG microstate features. Our results showed that the beta-band is the most useful EEG frequency sub-band as it performs best for classifying subjects. Also the results depicted that when the coverage feature of the EEG microstate analysis is calculated in beta-band, the classification shows fairly high accuracy and precision. Hence, the beta-band and the coverage are the most important features for classification of epilepsy and PNES patients.

Original languageEnglish
Article number6
Number of pages22
JournalBrain Informatics
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Dec 2020

Keywords

  • Classification
  • EEG microstate
  • Epilepsy
  • Functional network
  • PNES

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