Multi‐View Seizure Classification Based on Attention‐Based Adaptive Graph ProbSparse Hybrid Network

ABSTRACT Epilepsy is a neurological disorder characterised by recurrent seizures due to abnormal neuronal discharges. Seizure detection via EEG signals has progressed, but two main challenges are still encountered. First, EEG data can be distorted by physiological factors and external variables, resulting in noisy brain networks. Static adjacency matrices are typically used in current mainstream methods, which neglect the need for dynamic updates and feature refinement. The second challenge stems from the strong reliance on long‐range dependencies through self‐attention in current methods, which can introduce redundant noise and increase computational complexity, especially in long‐duration data. To address these challenges, the Attention‐based Adaptive Graph ProbSparse Hybrid Network (AA‐GPHN) is proposed. Brain network structures are dynamically optimised using variational inference and the information bottleneck principle, refining the adjacency matrix for improved epilepsy classification. A Linear Graph Convolutional Network (LGCN) is incorporated to focus on first‐order neighbours, minimising the aggregation of distant information. Furthermore, a ProbSparse attention‐based Informer (PAT) is introduced to adaptively filter long‐range dependencies, enhancing efficiency. A joint optimisation loss function is applied to improve robustness in noisy environments. Experimental results on both patient‐specific and cross‐subject datasets demonstrate that AA‐GPHN outperforms existing methods in seizure detection, showing superior effectiveness and generalisation.