Seizure Detection by a Novel Wavelet Packet Method

TitleSeizure Detection by a Novel Wavelet Packet Method
Publication TypeConference Paper
Year of Publication2006
AuthorsTafreshi, R., G. Dumont, D. Gross, C. R. Ries, E. Puil, and B. A. MacLeod
Conference NameEngineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Pagination6141 -6144
Date Publishedaug.
Keywordscross-data entropy algorithms, diseases, EEG signals, electroencephalography, medical signal processing, neurophysiology, relative entropy criterion, robust criterion, temporal lobe epileptic seizure detection, wavelet packet dictionary, wavelet packet method, wavelet transforms

We describe a novel wavelet-based method for the detection of seizure in patients with temporal lobe epilepsy. This method uses local discriminant bases and cross- data entropy algorithms to identify nodes of a wavelet packet dictionary that best discriminate preictal from ictal EEG signals. The algorithms are based on relative entropy criterion as a measure of discrepancy between different classes of signals. The frequency bands associated with these nodes were in the range of interest for seizure events. After selecting two bands, we determined the ratio of energies at the level of wavelet packet chosen by the cross-data entropy algorithm. Preliminary results demonstrate that the wavelet packet energy ratio could serve as a robust criterion in seizure detection


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