ECG-Based Seizure Prediction Using Progressive Feature Fusion Mamba

Seizure prediction is of paramount clinical importance for optimizing interventions and improving patient quality of life. Recent advancements in ECG-based seizure prediction have garnered attention due to their potential for portability and ease of data collection. However, existing methods primarily focus either on heart rate variability (HRV) to capture ECG trends or on convolutional neural networks (CNNs) for local feature extraction, but often fail to effectively combine both global and local information, limiting the prediction performance. To address this challenge, we propose the Progressive Feature Fusion Mamba (PFFMamba) network for ECG-based seizure prediction. PFFMamba enhances prediction accuracy in two key ways. First, it utilizes Mamba’s selective scanning mechanism to focus on important historical data while discarding irrelevant information, enabling the model to effectively capture subtle local variations and the long-term dependencies among them. Second, we introduce a progressive feature fusion structure to overcome Mamba’s limitation in combining parallel features and align with the hierarchical nature of features at different stages. This structure integrates frequency channels in the shallow layers, feature channels in the intermediate layers, and HRV representations in the deep layers, ensuring robust feature interaction throughout the network. By combining these strategies, PFFMamba extracts features that embody both long-term dependencies and multi-perspective representations. We evaluate our approach through seizure prediction experiments and demonstrate that PFFMamba achieves a sensitivity of 96.7%, a false prediction rate of 0.548/h, an AUC of 0.775 on the USTC Epileptic ECG database and a sensitivity of 95.9%, a false prediction rate of 0.641/h, an AUC of 0.724 on the Siena database, surpassing current state-of-the-art methods.