Title | A novel wavelet-based index to detect epileptic seizures using scalp EEG signals |
Publication Type | Journal Article |
Year of Publication | 2008 |
Authors | Zandi, A. S., G. A. Dumont, M. Javidan, R. Tafreshi, B. A. MacLeod, C. R. Ries, and E. Puil |
Journal | Conf Proc IEEE Eng Med Biol Soc |
Volume | 2008 |
Pagination | 919-22 |
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 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. |
URL | http://dx.doi.org/10.1109/IEMBS.2008.4649304 |
DOI | 10.1109/IEMBS.2008.4649304 |