aceADMP-GCN: Interpretable Adaptive Message Passing Graph Learning for Patient-specific EEG Seizure Prediction with Human-in-the-Loop Validation
Automated seizure prediction based on multichannel Electroencephalography (EEG) using Artificial Intelligence (AI) is a challenging task that mandates accurate and interpretable results for improved clinical decisionmaking.However, brain connectivity modelling on the evolving patient-specific and seizure-dynamic EEG signals makes the problem highly complex.Recently, Graph Neural Network (GNN)-based approaches have shown promising results for improved seizure prediction by modelling brain functional connectivity…
A topology-aware graph model with channel reduction and layered explanations reached 95.10% accuracy and 96.33% sensitivity on the CHB-MIT dataset, outperforming existing graph-based seizure predictors.
The source reports validation only on the CHB-MIT dataset, so it does not show performance in other populations or prospective clinical use.
Evidence
- Peer-reviewedInternational Journal of Intelligent Engineering and Systems2026-10-16
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Truvace Impact Record TRV-2026-1694, v1: “ADMP-GCN: Interpretable Adaptive Message Passing Graph Learning for Patient-specific EEG Seizure Prediction with Human-in-the-Loop Validation.” Truvace, 2026-10-17. /record/TRV-2026-1694 (accessed at citation time). sha256 b44d66778a5e3be9…
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