Norris, Jamie;
Chari, Aswin;
Van Blooijs, Dorien;
Cooray, Gerald K;
Friston, Karl;
Tisdall, Martin M;
Rosch, Richard E;
(2024)
Localising the Seizure Onset Zone from Single-Pulse
Electrical Stimulation Responses with a CNN Transformer.
In: Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo, (eds.)
Proceedings of the 9th Machine Learning for Healthcare Conference: PMLR.
(pp. pp. 1-21).
PMLR: Toronto, Canada.
Preview |
Text
norris24a.pdf - Published Version Download (1MB) | Preview |
Abstract
Epilepsy is one of the most common neurological disorders, often requiring surgical intervention when medication fails to control seizures. For effective surgical outcomes, precise localisation of the epileptogenic focus - often approximated through the Seizure Onset Zone (SOZ) - is critical yet remains a challenge. Active probing through electrical stimulation is already standard clinical practice for identifying epileptogenic areas. Our study advances the application of deep learning for SOZ localisation using Single-Pulse Electrical Stimulation (SPES) responses, with two key contributions. Firstly, we implement an existing deep learning model to compare two SPES analysis paradigms: divergent and convergent. These paradigms evaluate outward and inward effective connections, respectively. We assess the generalisability of these models to unseen patients and electrode placements using held-out test sets. Our findings reveal a notable improvement in moving from a divergent (AUROC: 0.574) to a convergent approach (AUROC: 0.666), marking the first application of the latter in this context. Secondly, we demonstrate the efficacy of CNN Transformers with cross-channel attention in handling heterogeneous electrode placements, increasing the AUROC to 0.730. These findings represent a significant step in modelling patient-specific intracranial EEG electrode placements in SPES. Future work will explore integrating these models into clinical decision-making processes to bridge the gap between deep learning research and practical healthcare applications.
Archive Staff Only
 |
View Item |
