Volta

An attention-guided graph spiking approach for seizure localization and detection in healthcare

Epilepsy is a chronic neurological disorder with recurrent seizures, posing significant challenges for timely diagnosis and treatment. The manual identification of seizures from long-term EEG is labour-intensive, time-consuming, and heavily dependent on expertise, which underscores the need for automated seizure detection systems. This study proposes a patient-specific hybrid Graph Neural Network–Spiking Neural Network (GNN–SNN) framework, integrating attention-driven channel importance estimation with graph-based spiking dynamics for interpretable seizure detection. The EEG channels are represented as graph nodes with attention layers modeling their spatial relationships, and Adaptive Leaky Integrate-and-Fire neurons represent biologically inspired temporal dynamics. A key feature of the framework is its capacity to measure channel-wise contributions through attention weights, which yields human-interpretable information about which EEG channels contribute most to …

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