Adaptive Multi-scale Spatiotemporal Mixing Network for Multi-view Seizure Detection

Epileptic seizure detection from Electroencephalography (EEG) signals is challenging due to their complex temporal dynamics and intricate inter-channel dependencies. A key difficulty lies in capturing multi-scale temporal features that span different time ranges. Seizure-related EEG patterns include rapidly varying micro-scale features and longer-duration macro-scale features, and modeling them jointly often leads to feature interference, hindering accurate temporal representation. In addition, many existing approaches fail to effectively capture spatial relationships between brain regions, further limiting detection performance. To cope with issues above, we design the Adaptive Multi-scale Spatiotemporal Mixing Network (AMSMN). The framework first decomposes EEG signals into macro- and micro-scale sequences, which are processed independently across multiple temporal resolutions to reduce cross-scale interference and better represent temporal dynamics. A spatial attention mechanism then fuses the decomposed features, ensuring that important inter-channel information is preserved. Finally, an Informer-based sparse attention layer captures long-range dependencies, allowing the model to focus on the most relevant global interactions across brain regions. We carry out experiments on two available databases demonstrate that AMSMN consistently achieves superior performance to prior methods in both patient-specific and cross-patient settings. The results confirm that the proposed framework improves seizure detection accuracy, robustness, and generalization by effectively integrating multi-scale temporal modeling with global spatial dependency extraction. This work advances EEG-based seizure detection by enabling precise multi-scale temporal analysis and efficient global dependency modeling, offering strong performance and generalizability for clinical applications.