A Contrastive Learning-Enhanced Residual Network for Predicting Epileptic Seizures Using EEG Signals.

Abstract

The models used to predict epileptic seizures based on electroencephalogram (EEG) signals often encounter substantial challenges due to the requirement for large, labeled datasets and the inherent complexity of EEG data, which hinders their robustness and generalization capability. This study proposes CLResNet, a framework for predicting epileptic seizures, which combines contrastive self-supervised learning with a modified deep residual neural network to address the above challenges. In contrast to traditional models, CLResNet uses unlabeled EEG data for pre-training to extract robust feature representations. It is then fine-tuned on a smaller labeled dataset to significantly reduce its reliance on labeled data while improving its efficiency and predictive accuracy. The contrastive learning (CL) framework enhances the ability of the model to distinguish between preictal and interictal states, thus improving its robustness and generalizability. The architecture of CLResNet contains residual connections that enable it to learn deep features of the data and ensure an efficient gradient flow. The results of the evaluation of the model on the CHB-MIT dataset showed that it outperformed prevalent methods in the field, with an accuracy of 92.97%, sensitivity of 94.18%, and false-positive rate of 0.043/h. On the Siena dataset, the model also achieved competitive performance, with an accuracy of 92.79%, a sensitivity of 91.47%, and a false-positive rate of 0.041/h. These results confirm the effectiveness of CLResNet in addressing variations in EEG data, and show that contrastive self-supervised learning is a robust and accurate approach for predicting seizures.