Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a CNN Transformer
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914925301989376 |
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| author | Norris, Jamie Chari, Aswin van Blooijs, Dorien Cooray, Gerald Friston, Karl Tisdall, Martin Rosch, Richard |
| author_facet | Norris, Jamie Chari, Aswin van Blooijs, Dorien Cooray, Gerald Friston, Karl Tisdall, Martin Rosch, Richard |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_20324 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a CNN Transformer Norris, Jamie Chari, Aswin van Blooijs, Dorien Cooray, Gerald Friston, Karl Tisdall, Martin Rosch, Richard Machine Learning Signal Processing Neurons and Cognition 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. |
| title | Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a CNN Transformer |
| topic | Machine Learning Signal Processing Neurons and Cognition |
| url | https://arxiv.org/abs/2403.20324 |