A new parametric feature descriptor for the classification of epileptic and control eeg records in pediatric population

Mercedes Cabrerizo, Melvin Ayala, Mohammed Goryawala, Prasanna Jayakar, Malek Adjouadi

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

This study evaluates the sensitivity, specificity and accuracy in associating scalp EEG to either control or epileptic patients by means of artificial neural networks (ANNs) and support vector machines (SVMs). A confluence of frequency and temporal parameters are extracted from the EEG to serve as input features to well-configured ANN and SVM networks. Through these classification results, we thus can infer the occurrence of high-risk (epileptic) as well as low risk (control) patients for potential follow up procedures.

Original languageEnglish (US)
Article number1250001
JournalInternational Journal of Neural Systems
Volume22
Issue number2
DOIs
StatePublished - Apr 2012
Externally publishedYes

Keywords

  • artificial neural networks
  • EEG classification
  • EEG data
  • epileptic versus control EEG
  • support vector machines
  • temporal and frequency parameters

ASJC Scopus subject areas

  • Computer Networks and Communications

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