Epileptic Seizure Prediction Using Multi-Strategy Data Augmentation and Hierarchical Contrastive Learning.

Accurate early prediction of epileptic seizures is crucial for improving patients' quality of life. However, existing seizure prediction methods often rely on large-scale labeled datasets and face challenges in generalization and real-time performance. To address these issues, this study proposes an efficient seizure prediction framework that achieves high performance even with limited labeled data, significantly reducing dependence on extensive annotations. To better distinguish preictal states, contrastive learning is employed to enhance feature separation between interictal and preictal periods, leading to improved sensitivity in detecting early seizure patterns. First, a data augmentation strategy is designed, incorporating wavelet-based frequency mixing, temporal masking, and window-based masking to enhance model robustness and generalization. Second, a hierarchical contrastive loss function is introduced, integrating instance-level and temporal contrastive learning to improve the model's ability to capture preictal patterns. Finally, a lightweight SE-EEGNet is developed and optimized as a feature extractor, strengthening critical feature extraction and enabling real-time seizure prediction. On the CHB-MIT dataset, the proposed method achieves 94.51% accuracy, 95.05% sensitivity, a 0.024/h false positive rate (FPR), and a 20.12-minute prediction time using only 30% labeled data. On the Siena dataset, it achieves 93.14% accuracy, 92.77% sensitivity, and a 0.030/h FPR. Moreover, performance improves further as the amount of labeled data increases, validating the effectiveness and practical applicability of the proposed approach in seizure prediction.