title: Scalp EEG brain functional connectivity networks in pediatric epilepsy.
abstract: This study establishes a new data-driven approach to brain functional connectivity networks using scalp EEG recordings for classifying pediatric subjects with epilepsy from pediatric controls. Graph theory is explored on the functional connectivity networks of individuals where three different sets of topological features were defined and extracted for a thorough assessment of the two groups. The rater's opinion on the diagnosis could also be taken into consideration when deploying the general linear model (GLM) for feature selection in order to optimize classification. Results demonstrate the existence of statistically significant (p<0.05) changes in the functional connectivity of patients with epilepsy compared to those of control subjects. Furthermore, clustering results demonstrate the ability to discriminate pediatric epilepsy patients from control subjects with an initial accuracy of 87.5%, prior to initiating the feature selection process and without taking into consideration the clinical rater's opinion. Otherwise, leave-one-out cross validation (LOOCV) showed a significant increase in the classification accuracy to 96.87% in epilepsy diagnosis.
authors.affiliation: Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA. Electronic address: ssarg004@fiu.edu.
authors.forename: Saman
authors.initials: S
authors.lastname: Sargolzaei
authors.affiliation: Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
authors.forename: Mercedes
authors.initials: M
authors.lastname: Cabrerizo
authors.affiliation: Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Goryawala
authors.affiliation: Florida Polytechnic University, Lakeland, FL, USA.
authors.forename: Anas Salah
authors.initials: AS
authors.lastname: Eddin
authors.affiliation: Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
authors.forename: Malek
authors.initials: M
authors.lastname: Adjouadi
mesh_terms: D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D012535:Scalp; D012815:Signal Processing, Computer-Assisted
keywords: Epilepsy; Functional connectivity; Graph theory; Pediatric; Scalp EEG