Feature fusion based on global-local weighted attention model for automatic epileptic seizure detection

Objective. Epilepsy is a neurological disorder characterized by recurrent seizures, which present significant challenges in both diagnosis and treatment. Despite advances in seizure detection, existing methods often struggle with accurately capturing the complex and dynamic interactions between temporal, spatial, and spectral features of EEG signals. This leads to limitations in the detection accuracy and generalization across different datasets. Approach. To address these challenges, we propose GLWA (Global-Local Weighted Attention) model, which integrates temporal, spatial, and spectral features through a local-global attention mechanism. At the same time, GLWA effectively balances both global and local features, capturing comprehensive information from EEG signals to enhance seizure detection accuracy. Main results. Our proposed model achieves accuracy rates of 98.82% and 98.89% on the CHB-MIT and Siena datasets, respectively. These results demonstrate the model's capability to effectively integrate these features, resulting in improved detection performance. Significance. Furthermore, we visualize the model's decision-making process to gain insights into the attention distribution across different brain regions and spectraluency bands, further emphasizing GLWA's potential in seizure detection. This work demonstrates the model's superior performance and interpretability, providing a robust approach for accurate and generalizable identification of seizures.