Graph Neural Networks in Epilepsy Surgery

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but.event.date23.04.2024cs
but.event.titleSTUDENT EEICT 2024cs
dc.contributor.authorHrtonová, Valentina
dc.contributor.authorFilipenská, Marina
dc.contributor.authorKlimeš, Petr
dc.date.accessioned2024-07-09T07:47:47Z
dc.date.available2024-07-09T07:47:47Z
dc.date.issued2024cs
dc.description.abstractEpilepsy 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.en
dc.formattextcs
dc.format.extent57-60cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers. s. 57-60. ISBN 978-80-214-6230-4cs
dc.identifier.doi10.13164/eeict.2024.57
dc.identifier.isbn978-80-214-6230-4
dc.identifier.issn2788-1334
dc.identifier.urihttps://hdl.handle.net/11012/249280
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectgraph neural networksen
dc.subjectdeep learningen
dc.subjectepilepsyen
dc.subjectintracranial EEGen
dc.subjectepileptogenic zoneen
dc.subjectseizure-onset zoneen
dc.subjectinterictal biomarkersen
dc.titleGraph Neural Networks in Epilepsy Surgeryen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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