title: The effects of high-frequency oscillations in hippocampal electrical activities on the classification of epileptiform events using artificial neural networks.
abstract: The existence of hippocampal high-frequency electrical activities (greater than 100 Hz) during the progression of seizure episodes in both human and animal experimental models of epilepsy has been well documented (Bragin A, Engel J, Wilson C L, Fried I and Buzsáki G 1999 Hippocampus 9 137-42; Khosravani H, Pinnegar C R, Mitchell J R, Bardakjian B L, Federico P and Carlen P L 2005 Epilepsia 46 1-10). However, this information has not been studied between successive seizure episodes or utilized in the application of seizure classification. In this study, we examine the dynamical changes of an in vitro low Mg2+ rat hippocampal slice model of epilepsy at different frequency bands using wavelet transforms and artificial neural networks. By dividing the time-frequency spectrum of each seizure-like event (SLE) into frequency bins, we can analyze their burst-to-burst variations within individual SLEs as well as between successive SLE episodes. Wavelet energy and wavelet entropy are estimated for intracellular and extracellular electrical recordings using sufficiently high sampling rates (10 kHz). We demonstrate that the activities of high-frequency oscillations in the 100-400 Hz range increase as the slice approaches SLE onsets and in later episodes of SLEs. Utilizing the time-dependent relationship between different frequency bands, we can achieve frequency-dependent state classification. We demonstrate that activities in the frequency range 100-400 Hz are critical for the accurate classification of the different states of electrographic seizure-like episodes (containing interictal, preictal and ictal states) in brain slices undergoing recurrent spontaneous SLEs. While preictal activities can be classified with an average accuracy of 77.4 +/- 6.7% utilizing the frequency spectrum in the range 0-400 Hz, we can also achieve a similar level of accuracy by using a nonlinear relationship between 100-400 Hz and <4 Hz frequency bands only.
authors.affiliation: Institute of Biomaterials and Biomedical Engineering, University of Toronto, 4 Taddle Creek Road, Toronto M5S 3G9, Ontario, Canada.
authors.forename: Alan W L
authors.initials: AW
authors.lastname: Chiu
authors.forename: Shokrollah S
authors.initials: SS
authors.lastname: Jahromi
authors.forename: Houman
authors.initials: H
authors.lastname: Khosravani
authors.forename: Peter L
authors.initials: PL
authors.lastname: Carlen
authors.forename: Berj L
authors.initials: BL
authors.lastname: Bardakjian
mesh_terms: D000200:Action Potentials; D000818:Animals; D001185:Artificial Intelligence; D001683:Biological Clocks; D001921:Brain / Q000503:physiopathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D006624:Hippocampus / Q000503:physiopathology*; D066298:In Vitro Techniques; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D017208:Rats, Wistar