Communication Dans Un Congrès Année : 2025

Leveraging System Identification Techniques for SEEG-Based Epileptogenic Zone Detection

Résumé

Accurate localization of epileptogenic tissue is paramount in epilepsy surgery. This paper proposes a novel approach using system identification techniques to analyze stereo-electroencephalographic (SEEG) recordings for improved epileptogenic zone (EZ) delineation. By treating adjacent SEEG contacts as coupled systems, we extract frequency response characteristics that capture local network dynamics. Transfer function features fed an XGBoost-based classifier to differentiate between electrode contacts within and outside the EZ. Using ictal data from 11 drug-resistant epilepsy patients who reached seizure freedom post-surgery, our system identification approach achieved 58% sensitivity and 70% specificity, outperforming reference biomarkers in accuracy, F1-score and Index of Balanced Accuracy. Moreover, our method delivers higher computational efficiency and operates independently of both signal amplitude and sampling frequency. These results suggest that system identification features could provide valuable complementary information for EZ delineation in clinical practice.

Fichier non déposé

Dates et versions

hal-05426525 , version 1 (19-12-2025)

Identifiants

Citer

Valentin Ricordel, Alina Voda, Gildas Besancon, Philippe Kahane. Leveraging System Identification Techniques for SEEG-Based Epileptogenic Zone Detection. ECC 2025 - 23rd European Control Conference, Jun 2025, Thessaloniki, Greece. pp.570-575, ⟨10.23919/ECC65951.2025.11187073⟩. ⟨hal-05426525⟩
37 Consultations
0 Téléchargements

Altmetric

Partager

  • More