Hybrid Attention-Enhanced Regularized Convolutional Autoencoder for Robust EEG Feature Extraction in Seizure Prediction

Electroencephalography (EEG)-based seizure prediction has garnered significant attention in epilepsy management, with deep learning methods enhancing prediction performance. However, persistent challenges include insufficient robust low-dimensional representations and poor generalization across datasets. Consequently, this study proposes a novel hybrid attention-enhanced regularized convolutional autoencoder (HA-RCAE) feature extraction method that generates low-dimensional features, effectively preserving critical biological information while mitigating technical batch effects in EEG signals. The HA-RCAE is augmented with regularization techniques and a novel hybrid attention mechanism for enhanced feature extraction. Coupled with classic machine learning classifiers, the proposed method outperforms advanced techniques on two epilepsy EEG datasets. On the Kaggle dataset, the model achieves area under the curve (AUC), sensitivity (SS), specificity (SP), and false positive rate (FPR) values of 90.6%, 89.1%, 89.8%, and 0.186/h, respectively; on the CHB-MIT dataset, it achieves AUC, SS, SP, and FPR values of 92.7%, 85.4%, 91.7%, and 0.144/h, respectively. The proposed method exhibits excellent generalizability, characterized by minimal performance discrepancies across datasets. Furthermore, its applicability extends to EEG anomaly detection, demonstrating superior performance and generalizability across clinical tasks. This comprehensive approach offers a robust solution for seizure prediction and other clinical applications, distinguished by strong generalization and negligible batch effects, rendering it promising for clinical implementation.