A novel wavelet-based index to detect epileptic seizures using scalp EEG signals.
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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.
Annu Int Conf IEEE Eng Med Biol Soc
author list (cited authors)
Zandi, A. S., Dumont, G. A., Javidan, M., Tafreshi, R., MacLeod, B. A., Ries, C. R., & Puil, E
complete list of authors
Zandi, Ali Shahidi||Dumont, Guy A||Javidan, Manouchehr||Tafreshi, Reza||MacLeod, Bernard A||Ries, Craig R||Puil, Ernie