A novel wavelet-based index to detect epileptic seizures using scalp EEG signals
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abstract
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 11.5 hours of multichannel scalp EEG recordings from three patients and compared it to our previously proposed waveletbased index. In this dataset, the novel combined index detected all epileptic seizures with a false detection rate of 0.52/hr. 2008 IEEE.
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
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, AS||Dumont, GA||Javidan, M||Tafreshi, R||MacLeod, BA||Ries, CR||Puil, E