Deep Learning Method Using Temporal–Spectral–Spatial Feature Fusion for Epileptic Seizure Prediction

This is the authors' abstract. We don't add key points for this paper.

Background/Objectives: Epilepsy is a recurrent cerebral disorder, and the sudden onset of seizures can result in accidental injuries or even life-threatening events for patients. In clinical practice, epileptic seizure prediction can provide warnings prior to the onset of seizures, alerting patients to take safety precautions and assisting neurologists in implementing timely interventions to suppress seizures. Therefore, accurate prediction is crucial for improving patients’ quality of life and clinical treatment outcomes. However, due to insufficient extraction of effective features from epileptic signals, the performance of prediction models is often limited. Methods: In this study, a novel deep learning model was proposed that integrated temporal–spectral–spatial features to address this limitation. Wavelet convolutions and temporal convolutions were employed to achieve the temporal–spectral representation network. Subsequently, the fused features were further processed using bidirectional long short-term memory networks and a dynamic multigraph attention network based on time slices to extract temporal and spatial features for seizure prediction. Results: The performance of the proposed model was comprehensively evaluated on the CHB-MIT and Siena databases using both segment-based and event-based evaluations. For the CHB-MIT database, the model achieved a sensitivity of 96.09 ± 8.83% and a false prediction rate of 0.021 ± 0.032/h. For the Siena database, the model demonstrated a sensitivity of 95.71 ± 7.87% and a false prediction rate of 0.028 ± 0.036/h. Furthermore, this study achieved patient-independent experiments for prediction evaluation. Conclusions: The proposed model effectively achieves the fusion of multiple features and significantly enhances the prediction performance through multiple deep learning methods. For patient-independent experiments, the excellent prediction performance demonstrates the strong generalization ability.