A novel wavelet-based index to detect epileptic seizures using scalp EEG signals

TitleA novel wavelet-based index to detect epileptic seizures using scalp EEG signals
Publication TypeJournal Article
Year of Publication2008
AuthorsZandi, A. S., G. A. Dumont, M. Javidan, R. Tafreshi, B. A. MacLeod, C. R. Ries, and E. Puil
JournalConf Proc IEEE Eng Med Biol Soc

In this paper, we propose a novel wavelet-based algorithm for the detection of epileptic seizures. The algorithm is based on the recognition of rhythmic activities associated with ictal states in surface EEG recordings. Using a moving-window analysis, we first decomposed each EEG segment into a wavelet packet tree. Then, we extracted the coefficients corresponding to the frequency band of interest defined for rhythmic activities. Finally, a normalized index sensitive to both the rhythmicity and energy of the EEG signal was derived, based on the resulting coefficients. In our study, we evaluated this combined index for real-time detection of epileptic seizures using a dataset of approximately 11.5 hours of multichannel scalp EEG recordings from three patients and compared it to our previously proposed wavelet-based index. In this dataset, the novel combined index detected all epileptic seizures with a false detection rate of 0.52/hr.


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