Zou Yonggang, Wang Peng, Du Lidong, Chen Xianxiang, Li Zhenfeng, Song Junxian, Fang Zhen
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China.
Bioengineering (Basel). 2025 Jan 8;12(1):44. doi: 10.3390/bioengineering12010044.
Atrial fibrillation (AF) is the most common persistent arrhythmia, and it is crucial to develop generalizable automatic AF detection methods. However, supervised AF detection is often limited in performance due to the difficulty in obtaining labeled data. To address the gap between limited labeled data and the requirements for model robustness and generalization in single-lead ECG AF detection, we proposed a semi-supervised contrastive learning method named MLMCL for AF detection. The MLMCL method utilizes the multi-level feature representations of the encoder to perform multiple contrastive learning to fully exploit temporal consistency, channel consistency, and label consistency. Meanwhile, it combines labeled and unlabeled data for pre-training to obtain robust features for downstream tasks. In addition, it uses the domain knowledge in the field of AF diagnosis for domain knowledge augmentation to generate hard samples and improve the distinguishability of ECG representations. In the cross-dataset testing mode, MLMCL had better performance and good stability on different test sets, demonstrating its effectiveness and robustness in the AF detection task. The comparison results with existing studies show that MLMCL outperformed existing methods in external tests. The MLMCL method can be extended and applied to multi-lead scenarios and has reference significance for the development of contrastive learning methods for other arrhythmia.
心房颤动（AF）是最常见的持续性心律失常，开发可推广的自动房颤检测方法至关重要。然而，由于获取标注数据困难，有监督的房颤检测在性能上往往受到限制。为了解决单导联心电图房颤检测中有限的标注数据与模型鲁棒性和泛化性要求之间的差距，我们提出了一种名为MLMCL的半监督对比学习方法用于房颤检测。MLMCL方法利用编码器的多级特征表示进行多次对比学习，以充分利用时间一致性、通道一致性和标签一致性。同时，它结合标注数据和未标注数据进行预训练，以获得用于下游任务的鲁棒特征。此外，它利用房颤诊断领域的领域知识进行领域知识增强，以生成困难样本并提高心电图表示的可区分性。在跨数据集测试模式下，MLMCL在不同测试集上具有更好的性能和良好的稳定性，证明了其在房颤检测任务中的有效性和鲁棒性。与现有研究的比较结果表明，MLMCL在外部测试中优于现有方法。MLMCL方法可扩展并应用于多导联场景，对其他心律失常对比学习方法的发展具有参考意义。
