A novel ST-GCN model based on homologous microstate for subject-independent seizure prediction.

Due to the lack of validated universal seizure markers, population-level prediction methods often exhibit limited performance. This study proposes homologous microstate dynamic attributes as a generalized, subject-independent seizure marker. Homologous microstate dynamic attributes were extracted using a novel spatiotemporal graph convolutional network (ST-GCN) model for subject-independent seizure prediction. An online deployment stage was introduced to validate the model's clinical applicability. The online deployment stage demonstrated that the model achieved sensitivities of 96.79% and 98.84% on the private dataset and Siena dataset, respectively. The ST-GCN model successfully predicts seizures in a subject-independent manner, demonstrating its potential as a generalized tool for seizure prediction in clinical settings. This study indicates that dynamics within homologous microstates can serve as a universal predictive biomarker for seizures, expanding microstate research beyond transition patterns. It also provides a practical template for clinical seizure prediction models.