Circadian dynamics of high frequency oscillations in patients with epilepsy

0472661 - FGÚ 2017 RIV IT eng C - Conference Paper (international conference)
Balach, J. - Ježdík, P. - Janča, R. - Čmejla, R. - Kršek, P. - Marusič, P. - Jiruška, Přemysl
Circadian dynamics of high frequency oscillations in patients with epilepsy.
Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS. Rome: SciTePress, 2016, s. 284-289. ISBN 978-989758170-0.
[BIOSIGNALS 2016 - International Conference on Bio-Inspired Systems and Signal Processing /9./,Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016. Rome (IT), 21.02.2016-23.02.2016]
R&D Projects: GA MZd(CZ) NT14489; GA ČR(CZ) GA14-02634S; GA MZd(CZ) NV15-29835A
Institutional support: RVO:67985823
Keywords : circadian rhythms * epilepsy * high-frequency oscillations * intracerebral EEG * seizure onset zone
Subject RIV: FH - Neurology

High frequency oscillations (HFOs) are novel biomarker of epileptogenic tissue. HFOs are currently used to localize the seizure generating areas of the brain, delineate the resection and to monitor the disease activity. It is well established that spatiotemporal dynamics of HFOs can be modified by sleep-wake cycle. In this study we aimed to evaluate in detail circadian and ultradian changes in HFO dynamics using techniques of automatic HFO detection. For this purpose we have developed and implemented novel algorithm to automatic detection and analysis of HFOs in long-term intracranial recordings of six patients. In 5/6 patients HFO rates significantly increased during NREM sleep. The largest NREM related increase in HFO rates were observed in brain areas which spatially overlapped with seizure onset zone. Analysis of long-term recording revealed existence of ultradian changes in HFO dynamics. This study demonstrated reliability of automatic HFO detection in the analysis of long-term intracranial recordings in humans. Obtained results can foster practical implementation of automatic HFO detecting algorithms into presurgical examination, dramatically decrease human labour and increase the information yield of HFOs.
Permanent Link: http://hdl.handle.net/11104/0269944