The class imbalance problem in automatic localization of the epileptogenic zone for epilepsy surgery: a systematic review

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F25%3A00637184" target="_blank" >RIV/68081731:_____/25:00637184 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/25:00142157

Result on the web

<a href="https://iopscience.iop.org/article/10.1088/1741-2552/ade28c" target="_blank" >https://iopscience.iop.org/article/10.1088/1741-2552/ade28c</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1741-2552/ade28c" target="_blank" >10.1088/1741-2552/ade28c</a>

Result language

angličtina

Original language name

The class imbalance problem in automatic localization of the epileptogenic zone for epilepsy surgery: a systematic review

Original language description

Objective. Accurate localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery, but the class imbalance of epileptogenic vs. non-epileptogenic electrode contacts in intracranial electroencephalography (iEEG) data poses significant challenges for automatic localization methods. This review evaluates methodologies for handling the class imbalance in EZ localization studies that use machine learning (ML). Approach. We systematically reviewed studies employing ML to localize the EZ from iEEG data, focusing on strategies for addressing class imbalance in data handling, algorithm design, and evaluation. Results. Out of 2,128 screened studies, 35 fulfilled the inclusion criteria. Across the studies, the iEEG contacts annotated as epileptogenic prior to automatic localization constituted a median of 18.34% of all contacts. However, many of these studies did not adequately address the class imbalance problem. Techniques such as data resampling and cost-sensitive learning were used to mitigate the class imbalance problem, but the chosen evaluation metrics often failed to account for it. Significance. Class imbalance significantly impacts the reliability of EZ localization models. More comprehensive management and innovative approaches are needed to enhance the robustness and clinical utility of these models. Addressing class imbalance in ML models for EZ localization will improve both the predictive performance and reliability of these models.

Czech name

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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

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OECD FORD branch

30103 - Neurosciences (including psychophysiology)

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

Journal of Neural Engineering

ISSN

1741-2560

e-ISSN

1741-2552

Volume of the periodical

22

Issue of the periodical within the volume

3

Country of publishing house

GB - UNITED KINGDOM

Number of pages

17

Pages from-to

031002

UT code for WoS article

001517750800001

EID of the result in the Scopus database

2-s2.0-105009361563