Timing matters for accurate identification of the epileptogenic zone

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Publikace nespadá pod Fakultu sportovních studií, ale pod Lékařskou fakultu. Oficiální stránka publikace je na webu muni.cz.
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CHYBOWSKI Bartlomiej KLIMES Petr CIMBÁLNÍK Jan TRAVNICEK Vojtech NEJEDLY Petr PAIL Martin PETER-DEREX Laure HALL Jeff JURAK Pavel BRÁZDIL Milan FRAUSCHER Birgit

Rok publikování 2024
Druh Článek v odborném periodiku
Časopis / Zdroj Clinical Neurophysiology
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
www https://www.sciencedirect.com/science/article/abs/pii/S1388245724000312?via%3Dihub
Doi http://dx.doi.org/10.1016/j.clinph.2024.01.007
Klíčová slova iEEG; Epilepsy; Seizure
Přiložené soubory
Popis Objective: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG). Methods: We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC). Results: On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments. Conclusions: The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep. Significance: Random selection of short iEEG segments may give rise to inaccurate localization of the EZ. (c) 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
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