Book chapter
Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Nonepileptic Seizure and Complex Partial Seizure Patients
Data Mining for Biomarker Discovery, pp 57-77
07 Jan 2012
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.
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1 citations in Scopus
Details
- Title
- Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Nonepileptic Seizure and Complex Partial Seizure Patients
- Creators
- Jui-Hong Chien - Optima NeuroscienceDeng-Shan Shiau - Optima NeuroscienceJ. Chris Sackellares - Optima NeuroscienceJonathan J. Halford - Medical University of South CarolinaKevin M. Kelly - Drexel University College of Medicine, Allegheny-Singer Research Institute, Allegheny General HospitalPanos M. Pardalos - University of Florida Health
- Publication Details
- Data Mining for Biomarker Discovery, pp 57-77
- Series
- Springer Optimization and Its Applications
- Publisher
- Springer US; Boston, MA
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Neurology
- Scopus ID
- 2-s2.0-85103587299
- Other Identifier
- 991019173824504721