Application of Higher Order Spectra to Identify Epileptic EEG |
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Authors: | Kuang Chua Chua V Chandran U Rajendra Acharya and C M Lim |
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Institution: | (1) School of Engineering, Division of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore;(2) School of Engineering Systems, Faculty of Built Environment and Engineering, Queensland University of Technology, Gardens Point, 2 George Street, Brisbane, Queensland, 4001, Australia |
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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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