title: Seizure detection by a novel wavelet packet method.
abstract: 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.
authors.affiliation: Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC, Canada. rezat@ece.ubc.ca
authors.forename: Reza
authors.initials: R
authors.lastname: Tafreshi
authors.forename: Guy
authors.initials: G
authors.lastname: Dumont
authors.forename: Donald
authors.initials: D
authors.lastname: Gross
authors.forename: Craig R
authors.initials: CR
authors.lastname: Ries
authors.forename: Ernie
authors.initials: E
authors.lastname: Puil
authors.forename: Bern A
authors.initials: BA
authors.lastname: MacLeod
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D044962:Data Compression; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D004827:Epilepsy; D005583:Fourier Analysis; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012640:Seizures; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012984:Software; D014584:User-Computer Interface