Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., 420500 Innopolis, The Republic of Tatarstan, Russia.
University of Münster, Institute of Physiology I, Münster, 48149, Germany.
Sci Rep. 2019 May 10;9(1):7243. doi: 10.1038/s41598-019-43619-3.
The use of extreme events theory for the analysis of spontaneous epileptic brain activity is a relevant multidisciplinary problem. It allows deeper understanding of pathological brain functioning and unraveling mechanisms underlying the epileptic seizure emergence along with its predictability. The latter is a desired goal in epileptology which might open the way for new therapies to control and prevent epileptic attacks. With this goal in mind, we applied the extreme event theory for studying statistical properties of electroencephalographic (EEG) recordings of WAG/Rij rats with genetic predisposition to absence epilepsy. Our approach allowed us to reveal extreme events inherent in this pathological spiking activity, highly pronounced in a particular frequency range. The return interval analysis showed that the epileptic seizures exhibit a highly-structural behavior during the active phase of the spiking activity. Obtained results evidenced a possibility for early (up to 7 s) prediction of epileptic seizures based on consideration of EEG statistical properties.
极端事件理论在分析自发性癫痫脑活动中的应用是一个相关的多学科问题。它可以帮助我们更深入地了解病理性大脑功能，并揭示癫痫发作出现及其可预测性的机制。后者是癫痫学的一个理想目标，可能为控制和预防癫痫发作开辟新的治疗途径。考虑到这一目标，我们应用极端事件理论研究了具有遗传性失神癫痫倾向的 WAG/Rij 大鼠脑电图（EEG）记录的统计特性。我们的方法使我们能够揭示这种病理性尖峰活动中固有的极端事件，这些事件在特定频率范围内表现得非常明显。返回间隔分析表明，癫痫发作在尖峰活动的活跃期表现出高度结构化的行为。所得到的结果表明，基于 EEG 统计特性的考虑，可以实现对癫痫发作的早期（高达 7 秒）预测。
