Timing matters for accurate identification of the epileptogenic zone
The result's identifiers
Result code in 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>
Alternative codes found
RIV/00159816:_____/24:00081585 RIV/00216224:14110/24:00136069
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Timing matters for accurate identification of the epileptogenic zone
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30210 - Clinical neurology
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Clinical Neurophysiology
ISSN
1388-2457
e-ISSN
1872-8952
Volume of the periodical
161
Issue of the periodical within the volume
May
Country of publishing house
IE - IRELAND
Number of pages
9
Pages from-to
1-9
UT code for WoS article
001208751200001
EID of the result in the Scopus database
2-s2.0-85188190982