Functional connectivity network based on graph analysis of scalp EEG for epileptic classification

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

The proposed study presents a novel fully automated data-driven approach for differentiating epileptic subjects from normal controls using graph-based functional connectivity networks calculated using scalp EEG. A set of fourteen density-related, graph distance-based and spectral topological features extracted from the network graph is employed for the classification process. The proposed algorithm demonstrated an accuracy of 87.5% with a sensitivity of 75% and specificity of 100% when tested on 8 subjects. The study showed that graph-based functional connectivity networks in epileptic subjects were significantly different from those of controls (p<0.05). The study has the potential for aiding neurologists in decision making for diagnostic purposes solely based on scalp EEG.

Original languageEnglish (US)
Title of host publication2013 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2013
PublisherIEEE Computer Society
ISBN (Print)9781479930074
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2013 - Brooklyn, NY, United States
Duration: Dec 7 2013Dec 7 2013

Publication series

Name2013 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2013

Conference

Conference2013 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2013
Country/TerritoryUnited States
CityBrooklyn, NY
Period12/7/1312/7/13

Keywords

  • Epilepsy
  • Functional Connectivity
  • Graph Theory
  • Scalp EEG

ASJC Scopus subject areas

  • Biomedical Engineering

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