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Communication Dans Un Congrès Année : 2022

System-Identification-Based Automatic Brain Tissue Classification for Stereoelectroencephalography

Résumé

In the cases of drug-resistant epilepsy, patients might undergo resective surgery of the epileptic zone (EZ). The success of the surgery depends on the correct identification of the EZ and the eloquent cortex to be avoided. In both cases, the correct classification of the tissue where the measuring contacts are inserted is needed during the stereoelectroencephalography (SEEG). Most of the tissue classification procedures rely on imaging. In this paper a system identification based automatic classifier is proposed using previously proposed non-parametric and parametric methods for single contact tissue classification. By combining both identification methods, poorly classified contacts can be eliminated, and overall contact classification can be improved, especially for the parametric classifier. The proposed method can be either used in combination with imaging methods, or it could be used to help select contacts to be recorded during SEEG Investigation.
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Dates et versions

hal-03958186 , version 1 (26-01-2023)

Identifiants

Citer

Mariana Mulinari Pinheiro Machado, Alina Voda, Gildas Besancon, Guillaume Becq, Olivier David, et al.. System-Identification-Based Automatic Brain Tissue Classification for Stereoelectroencephalography. ICSTCC 2022 - 26th International Conference on System Theory, Control and Computing (ICSTCC), Oct 2022, Sinaia, Romania. pp.440-445, ⟨10.1109/ICSTCC55426.2022.9931852⟩. ⟨hal-03958186⟩
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