title: Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: a comparative study.
abstract: Epilepsy is characterized by the spontaneous and 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 these phenomena. The use of non-linear features motivated by the higher order spectra (HOS) had been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, the features are extracted from the power spectrum and the bispectrum. Their performance is studied by feeding them to a Gaussian mixture model (GMM) classifier. Results show that with selected HOS based features, we were able to achieve 93.11% compared to classification accuracy of 88.78% as that of features derived from PSD.
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore. ckc@np.edu.sg
authors.forename: K C
authors.initials: KC
authors.lastname: Chua
authors.forename: V
authors.initials: V
authors.lastname: Chandran
authors.forename: Rajendra
authors.initials: R
authors.lastname: Acharya
authors.forename: C M
authors.initials: CM
authors.lastname: Lim
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003199:Computer Systems; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods* / Q000706:statistics & numerical data*; D019277:Entropy; D004827:Epilepsy / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted