Application of higher order spectra to identify epileptic EEG

Chua, Kuang, Chandran, Vinod, Acharya, Rajendra, & Lim, C.M. (2010) Application of higher order spectra to identify epileptic EEG. Journal of Medical Systems, Online(Online), pp. 1-9.

View at publisher


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.

Impact and interest:

60 citations in Scopus
28 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 45507
Item Type: Journal Article
Refereed: Yes
Keywords: EEG, Epilepsy, Pre-ictal, Entropy, Bispectrum, Power spectrum, GMM, ROC
DOI: 10.1007/s10916-010-9433-z
ISSN: 0148-5598
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Deposited On: 29 Aug 2011 04:11
Last Modified: 15 Apr 2015 03:13

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page