title: A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks.
abstract: BACKGROUND
  The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment.
  
  
  METHODS
  A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group.
  
  
  RESULTS
  The study results show the existence of a statistically significant difference (p < 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis.
  
  
  CONCLUSIONS
  The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed.
authors.forename: Saman
authors.initials: S
authors.lastname: Sargolzaei
authors.forename: Mercedes
authors.initials: M
authors.lastname: Cabrerizo
authors.forename: Arman
authors.initials: A
authors.lastname: Sargolzaei
authors.forename: Shirin
authors.initials: S
authors.lastname: Noei
authors.forename: Anas
authors.initials: A
authors.lastname: Eddin
authors.forename: Hoda
authors.initials: H
authors.lastname: Rajaei
authors.forename: Alberto
authors.initials: A
authors.lastname: Pinzon-Ardila
authors.forename: Sergio M
authors.initials: SM
authors.lastname: Gonzalez-Arias
authors.forename: Prasanna
authors.initials: P
authors.lastname: Jayakar
authors.forename: Malek
authors.initials: M
authors.lastname: Adjouadi
mesh_terms: D000293:Adolescent; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D009415:Nerve Net / Q000502:physiology*; D011237:Predictive Value of Tests; D012535:Scalp / Q000473:pathology; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted