Statistical Properties and Predictability of Extreme Epileptic Events.

Autor: Frolov NS; Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., 420500 Innopolis, The Republic of Tatarstan, Russia., Grubov VV; Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., 420500 Innopolis, The Republic of Tatarstan, Russia., Maksimenko VA; Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., 420500 Innopolis, The Republic of Tatarstan, Russia., Lüttjohann A; University of Münster, Institute of Physiology I, Münster, 48149, Germany., Makarov VV; Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., 420500 Innopolis, The Republic of Tatarstan, Russia., Pavlov AN; Yuri Gagarin State Technical University of Saratov, 77 Politechnicheskaya str., 410054, Saratov, Russia., Sitnikova E; Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Science, Moscow, Russia., Pisarchik AN; Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., 420500 Innopolis, The Republic of Tatarstan, Russia.; Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain., Kurths J; Potsdam Institute for Climate Impact Research, 14473, Potsdam, Germany.; Department of Physics, Humboldt University, 12489, Berlin, Germany.; Biological Faculty, Saratov State University, Saratov, 410012, Russia., Hramov AE; Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., 420500 Innopolis, The Republic of Tatarstan, Russia. a.hramov@innopolis.ru.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2019 May 10; Vol. 9 (1), pp. 7243. Date of Electronic Publication: 2019 May 10.
DOI: 10.1038/s41598-019-43619-3
Abstrakt: 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.
Databáze: MEDLINE