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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Statistical Properties and Predictability of Extreme Epileptic Events

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Author(s):
Frolov, Nikita S. [1] ; Grubov, Vadim V. [1] ; Maksimenko, Vladimir A. [1] ; Luettjohann, Annika [2] ; Makarov, Vladimir V. [1] ; Pavlov, Alexey N. [3] ; Sitnikova, Evgenia [4] ; Pisarchik, Alexander N. [1, 5] ; Kurths, Juergen [6, 7, 8] ; Hramov, Alexander E. [1]
Total Authors: 10
Affiliation:
[1] Innopolis Univ, Neurosci & Cognit Technol Lab, 1 Univ Skaya Str, Innopolis 420500, Republic Of Tat - Russia
[2] Univ Munster, Inst Physiol 1, D-48149 Munster - Germany
[3] Yuri Gagarin State Tech Univ Saratov, 77 Politechn Skaya Str, Saratov 410054 - Russia
[4] Russian Acad Sci, Inst Higher Nervous Act & Neurophysiol, Moscow - Russia
[5] Tech Univ Madrid, Ctr Biomed Technol, Campus Montegancedo, Madrid 28223 - Spain
[6] Humboldt Univ, Dept Phys, D-12489 Berlin - Germany
[7] Potsdam Inst Climate Impact Res, D-14473 Potsdam - Germany
[8] Saratov NG Chernyshevskii State Univ, Biol Fac, Saratov 410012 - Russia
Total Affiliations: 8
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 9, MAY 10 2019.
Web of Science Citations: 0
Abstract

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. (AU)

FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants