Graph Neural Networks in Epilepsy Surgery
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Date
2024
Authors
Hrtonová, Valentina
Filipenská, Marina
Klimeš, Petr
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Mark
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Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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Abstract
Epilepsy surgery presents a viable treatment option for patients with drug-resistant epilepsy, necessitating precise localization of the epileptogenic zone (EZ) for optimal outcomes. As the limitations of currently used localization methods lead to a seizure-free postsurgical outcome only in about 60% of cases, this study introduces a novel approach to EZ localization by leveraging Graph Neural Networks (GNNs) for the analysis of interictal stereoelectroencephalography (SEEG) data. A GraphSAGE-based model for identifying resected seizure-onset zone (SOZ) electrode contacts was applied to a clinical dataset comprising 17 patients from two institutions. This study uniquely focuses on the use of interictal SEEG recordings, aiming to streamline the presurgical monitoring process and minimize risks and costs associated with prolonged SEEG monitoring. Through this innovative approach, the GNN model demonstrated promising results, achieving an Area Under the Receiver Operating Characteristic (AUROC) score of 0.830 and an Area Under the Precision-Recall Curve (AUPRC) of 0.432. These outcomes along with the potential of GNNs in leveraging the patient-specific electrode placement highlight their potential in enhancing the accuracy of EZ localization in drug-resistant epilepsy patients.
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Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers. s. 57-60. ISBN 978-80-214-6230-4
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdf
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en
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií