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Journal of Medical Systems
Erschienen in: Journal of Medical Systems 6/2011

01.12.2011 | Original Paper

Application of Higher Order Spectra to Identify Epileptic EEG

verfasst von: Kuang Chua Chua, V. Chandran, U. Rajendra Acharya, C. M. Lim

Erschienen in: Journal of Medical Systems | Ausgabe 6/2011

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Abstract

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.
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Metadaten
Titel
Application of Higher Order Spectra to Identify Epileptic EEG
verfasst von
Kuang Chua Chua
V. Chandran
U. Rajendra Acharya
C. M. Lim
Publikationsdatum
01.12.2011
Verlag
Springer US
Erschienen in
Journal of Medical Systems / Ausgabe 6/2011
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-010-9433-z

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