Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F25%3A00603248" target="_blank" >RIV/68081731:_____/25:00603248 - isvavai.cz</a>

Alternative codes found

RIV/00159816:_____/25:00082492 RIV/00216224:14110/25:00140603

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1388245724003304" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1388245724003304</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.clinph.2024.11.007" target="_blank" >10.1016/j.clinph.2024.11.007</a>

Result language

angličtina

Original language name

Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology

Original language description

Introduction: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack of standardized evaluation guidelines hinder the consistent evaluation of automatic machine learning localization models. Methods: This study addresses these challenges by analyzing class imbalance in clinical datasets and evaluating common assessment metrics. Data from 139 drug-resistant epilepsy patients across two Institutions were analyzed. Metric behaviors were examined using clinical and simulated data. Results: Complementary use of Area Under the Receiver Operating Characteristic (AUROC) and Area Under the Precision-Recall Curve (AUPRC) provides an optimal evaluation approach. This must be paired with an analysis of class imbalance and its impact due to significant variations found in clinical datasets. Conclusions: The proposed framework offers a comprehensive and reliable method for evaluating machine learning models in epileptogenic zone localization, improving their precision and clinical relevance. Significance: Adopting this framework will improve the comparability and multicenter testing of machine learning models in epileptogenic zone localization, enhancing their reliability and ultimately leading to better surgical outcomes for epilepsy patients.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30210 - Clinical neurology

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)

Publication year

2025

Confidentiality

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

Name of the periodical

Clinical Neurophysiology

ISSN

1388-2457

e-ISSN

1872-8952

Volume of the periodical

169

Issue of the periodical within the volume

January

Country of publishing house

IE - IRELAND

Number of pages

14

Pages from-to

33-46

UT code for WoS article

001370248100001

EID of the result in the Scopus database

2-s2.0-85210128489