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oai:recercat.cat:10230/696002025-12-19T20:25:58Zcom_2072_6col_2072_452954

urn:hdl:10230/69600 Prediction of seizure onset zone in epilepsy patients via a network coupling measure Elizondo Urrutia, Saioa Treball de fi de grau – Curs 2023-2024 Epilepsy Seizure EEG Seizure onset zone L measure Directional coupling Network analysis Graph theory Treball de Fi de Grau en Enginyeria Biomèdica. Curs 2023-2024 Tutor: Marc Grau Leguia Epilepsy, a chronic neurological disorder characterized by recurrent seizures, affects millions globally. For patients with drug-resistant epilepsy, surgical intervention becomes a viable option. However, precise localization of the seizure onset zone (SOZ) is crucial for successful surgery. This thesis investigates the potential of the L measure, a non-linear method analyzing directional couplings between brain regions, for SOZ detection in pharmacoresistant epilepsy patients using electroencephalography (EEG) data recorded in a natural environment. We analyzed seizure dynamics in 10 patients using EEG data from the Melbourne NeuroVista Seizure Prediction Trial database. Applying the L measure, we explored connectivity patterns within and across brain regions during pre-ictal, seizure onset, and ictal stages. Network analysis using graph theory metrics assessed these variations across EEG channels and patients to identify potential SOZ locations. Furthermore, we developed a novel method, to track channel connectivity dynamics during seizures, potentially detecting the SOZ with higher temporal resolution. These findings are expected to contribute to a more comprehensive understanding of seizure dynamics and the potential of the L measure for SOZ detection in pharmacoresistant epilepsy patients. This research may pave the way for improved surgical planning and treatment outcomes for this challenging patient population. 2025-02-13T10:26:08Z 2025-02-13T10:26:08Z 2024 info:eu-repo/semantics/bachelorThesis This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess