title: A new parametric feature descriptor for the classification of epileptic and control EEG records in pediatric population.
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.
authors.affiliation: Center for Advanced Technology and Education, College of Engineering and Computing, Florida International University, Miami, FL 33174, USA. cabreriz@fiu.edu
authors.forename: Mercedes
authors.initials: M
authors.lastname: Cabrerizo
authors.forename: Melvin
authors.initials: M
authors.lastname: Ayala
authors.forename: Mohammed
authors.initials: M
authors.lastname: Goryawala
authors.forename: Prasanna
authors.initials: P
authors.lastname: Jayakar
authors.forename: Malek
authors.initials: M
authors.lastname: Adjouadi
mesh_terms: D000293:Adolescent; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography; D004827:Epilepsy / Q000145:classification* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010372:Pediatrics; D010775:Photic Stimulation; D012372:ROC Curve; D060388:Support Vector Machine