Timing matters for accurate identification of the epileptogenic zone

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F24%3A00602818" target="_blank" >RIV/68081731:_____/24:00602818 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00159816:_____/24:00081585 RIV/00216224:14110/24:00136069

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1388245724000312" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1388245724000312</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.clinph.2024.01.007" target="_blank" >10.1016/j.clinph.2024.01.007</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Timing matters for accurate identification of the epileptogenic zone

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Timing matters for accurate identification of the epileptogenic zone

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30210 - Clinical neurology

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Clinical Neurophysiology

ISSN

1388-2457

e-ISSN

1872-8952

Svazek periodika

161

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

IE - Irsko

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

001208751200001

EID výsledku v databázi Scopus

2-s2.0-85188190982