GT-STAFG: Graph Transformer with Spatiotemporal Attention Fusion Gate for Epileptic Seizure Detection in Imbalanced EEG Data

Background: Electroencephalography (EEG) assists clinicians in diagnosing epileptic seizures by recording brain electrical activity. Existing models process spatiotemporal features inefficiently either through cascaded spatiotemporal architectures or static functional connectivity, limiting their ability to capture deeper spatial–temporal correlations. Objectives: To address these limitations, we propose a Graph Transformer with Spatiotemporal Attention Fusion Gate (GT-STAFG). Methods: We analyzed 18-channel EEG data sampled at 200 Hz, transformed into the frequency domain, and segmented into 30- second windows. The graph transformer exploits dynamic graph data, while STAFG leverages self-attention and gating mechanisms to capture complex interactions by augmenting graph features with both spatial and temporal information. The clinical significance of extracted features was validated using the Integrated Gradients attribution method, emphasizing the clinical relevance of the proposed model. Results: GT-STAFG achieves the highest area under the precision–recall curve (AUPRC) scores of 0.605 on the TUSZ dataset and 0.498 on the CHB-MIT dataset, surpassing baseline models and demonstrating strong cross-patient generalization on imbalanced datasets. We applied transfer learning to leverage knowledge from the TUSZ dataset when analyzing the CHB-MIT dataset, yielding an average improvement of 8.3 percentage points in AUPRC. Conclusions: Our approach has the potential to enhance patient outcomes and optimize healthcare utilization.