J Med Syst - Application of higher order spectra to identify epileptic EEG.

Tópicos

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Resumo

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.

Resumo Limpo

epilepsi character spontan seem unforese occurr seizur percept behavior patient disturb automat system detect seizur onset allow patient peopl near take appropri precaut provid insight phenomenon various method propos predict onset seizur base eeg record use nonlinear featur motiv higher order spectra hos report promis approach differenti normal background preictal epilept eeg signal work made compar studi perform gaussian mixtur model gmm support vector machin svm classifi use featur deriv hos power spectrum result show select hos base featur achiev classif accuraci compar featur deriv power spectrum gmm classifi svm classifi achiev improv featur base power spectrum featur base bispectrum

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