Explainable End-to-End Seizure Prediction via Dynamic Multiscale Cross-Band Fusion Filter Network

Epileptic seizure prediction based on electroencephalogram (EEG) signals is one of the critical applications of medical artificial intelligence (AI), with considerable clinical potential for improving the quality of life of patients through early warnings. However, existing prediction models face dual challenges: insufficient feature representation and limited explainability of the decision. To address these challenges, this study proposes a dynamic multiscale cross-band fusion filter network (MCFNet) for end-to-end seizure prediction. Specifically, the model first decomposes EEG signals into multiscale components and incorporates a cross-band fusion attention mechanism to achieve multi-granularity signal fusion. Subsequently, the synchronous spectral filtering network, comprising both static and dynamic filtering modules, is designed to capture the periodic components and cross-channel dependencies in EEG signals. Notably, two explainable methods are introduced: a joint feature visualization strategy and an efficient feature ablation analysis, helping to bridge the gap between the "black-box" nature of deep learning and clinical needs. Evaluated on the CHB-MIT dataset, MCFNet achieves a sensitivity of 97.13%, a specificity of 97.22%, and a false positive rate (FPR) of 0.0326/h. Experimental results show that MCFNet not only exhibits superior predictive performance but also maintains a low FPR, offering a feasible scheme for clinical application of EEG-based seizure prediction.