Scalp EEG brain functional connectivity networks in pediatric epilepsy

Saman Sargolzaei, Mercedes Cabrerizo, Mohammed Goryawala, Anas Salah Eddin, Malek Adjouadi

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

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. •Introducing a new data driven graph theory-based methodology for constructing brain functional connectivity networks.

Original languageEnglish (US)
Pages (from-to)158-166
Number of pages9
JournalComputers in Biology and Medicine
Volume56
DOIs
StatePublished - Jan 1 2015
Externally publishedYes

Keywords

  • Epilepsy
  • Functional connectivity
  • Graph theory
  • Pediatric
  • Scalp EEG

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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