Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Nonepileptic Seizure and Complex Partial Seizure Patients

Jui Hong Chien, Deng Shan Shiau, J. Chris Sackellares, Jonathan J. Halford, Kevin M. Kelly, Panos M. Pardalos

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Electroencephalography (EEG) is a technology for measuring brain neuronal activity and is used to investigate various pathological conditions of the brain. A brain can be viewed as a complex network of neurons. A brain functional network represents quantitative interactions among EEG channels and can be expressed as a graph. Graph theoretical analysis, therefore, can be applied to offer a broader scope to inspect the global functional network characteristics of epileptic brains and can reveal the existence of small-world network structure. In this study, we inspected the interhemispheric power asymmetry (IHPA) of interictal scalp EEG signals recorded from patients with epilepsy and psychogenic nonepileptic events and found significant differences between the two patient groups. Specifically, the degrees of IHPA in the two patient groups differed in signals from the frontal lobe regions in the delta, theta, alpha, and gamma frequency bands.

Original languageEnglish (US)
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer
Pages57-77
Number of pages21
DOIs
StatePublished - 2012

Publication series

NameSpringer Optimization and Its Applications
Volume65
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

Keywords

  • Functional Network
  • Power Asymmetry
  • Symmetric Pair
  • Temporal Lobe Epilepsy
  • Visual Working Memory

ASJC Scopus subject areas

  • Control and Optimization

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