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

Int J Neural Syst. 2012 Apr;22(2):1250001. doi: 10.1142/S0129065712500013.

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adolescent
  • Brain Mapping
  • Brain Waves / physiology*
  • Child
  • Child, Preschool
  • Electroencephalography*
  • Epilepsy / classification*
  • Epilepsy / physiopathology*
  • Female
  • Humans
  • Infant
  • Male
  • Models, Theoretical
  • Neural Networks, Computer
  • Pediatrics
  • Photic Stimulation
  • ROC Curve
  • Support Vector Machine