Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a CNN Transformer
<|reference_start|>Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a CNN Transformer: 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.<|reference_end|>
@article{norris2024localising,
    title={Localising the Seizure Onset Zone from Single-Pulse Electrical
  Stimulation Responses with a CNN Transformer},
    author={Jamie Norris, Aswin Chari, Dorien van Blooijs, Gerald Cooray, Karl
  Friston, Martin Tisdall, Richard Rosch},
    journal={arXiv preprint arXiv:2403.20324},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.20324},
    primaryClass={cs.LG eess.SP q-bio.NC}
}