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.


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