A Multi-Lead Electrocardiogram Signal Classification Method Based on Temporal and Multi-View Contrastive Learning

The rise in wearable devices has led to the generation of a large amount of unlabeled electrocardiogram (ECG) data. Effectively utilising this data has been a challenge. One approach to address this issue is contrastive learning. However, most existing contrastive learning methods based on data augmentation primarily utilize the augmented electro-cardiogram (ECG) signals for comparison. These approaches have certain drawbacks. While the augmented ECG signals may help highlight certain features, they could also potentially mask important variations in the original signal, leading to the loss of crucial information. Moreover, solely relying on augmented data may lead to the model relying too heavily on specific variation patterns, overlooking the authentic features present in the original signal, resulting in poor generalization and robustness for downstream tasks. To address this issue, we propose a multi-lead electrocardiogram signal classification method based on temporal and multi-view contrastive learning. The method utilizes the time invariance of ECG signals and the multi-view information provided by the original ECG signals and augmented signals for contrastive learning. Through this approach, it not only addresses the limitations of relying solely on augmented signals but also leverages the prior knowledge that ECG signal categories remain stable over short periods of time to fully model the time context of ECG signals, capturing dynamic features. The integration of the temporal context comparison module and the multi-view comparison module significantly enhances the performance of downstream classification tasks. The outcomes of our experiments show that our method performs better on four datasets than previous approaches, which even exceeds supervised performance. When we pretrain on the SPH dataset and fine-tune on the PTB-XL dataset, our approach shows the best performance. Specifically, our method's AUROC exceeds that of the best baseline model by 4.9% and surpasses supervised performance by 1.3%.