title: A comparison between automated detection methods of high-frequency oscillations (80-500 Hz) during seizures.
abstract: High-frequency oscillations (HFOs, ripples: 80-200 Hz, fast ripples: 250-500 Hz) recorded from the epileptic brain are thought to reflect abnormal network-driven activity. They are also better markers of seizure onset zones compared to interictal spikes. There is thus an increasing number of studies analysing HFOs in vitro, in vivo and in the EEG of human patients with refractory epilepsy. However, most of these studies have focused on HFOs during interictal events or at seizure onset, and few have analysed HFOs during seizures. In this study, we are comparing three different automated methods of HFO detection to two methods of visual analysis, during the pre-ictal, ictal and post-ictal periods on multiple channels using the rat pilocarpine model of temporal lobe epilepsy. The first method (method 1) detected HFOs using the average of the normalised period, the second (method 2) detected HFOs using the average of the normalised period in 1s windows and the third (method 3) detected HFOs using the average of a reference period before seizure onset. Overall, methods 2 and 3 showed higher sensitivity compared to method 1. When dividing the analysed traces in pre-, ictal and post-ictal periods, method 3 showed the highest sensitivity during the ictal period compared to method 1, while method 2 was not significantly different from method 1. These findings suggest that method 3 could be used for automated and reliable detection of HFOs on large data sets containing multiple channels during the ictal period.
authors.affiliation: Montreal Neurological Institute and Department of Neurology & Neurosurgery, McGill University, Montréal, H3A 2B4 QC, Canada.
authors.forename: Pariya
authors.initials: P
authors.lastname: Salami
authors.forename: Maxime
authors.initials: M
authors.lastname: Lévesque
authors.forename: Jean
authors.initials: J
authors.lastname: Gotman
authors.forename: Massimo
authors.initials: M
authors.lastname: Avoli
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods*; D051381:Rats; D017207:Rats, Sprague-Dawley; D012640:Seizures / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted