School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, China.
School of Physics and Technology, Wuhan University, Wuhan 430072, China.
Comput Methods Programs Biomed. 2024 Dec;257:108452. doi: 10.1016/j.cmpb.2024.108452. Epub 2024 Oct 9.
Electrocardiogram (ECG) is one of the most important diagnostic tools for cardiovascular diseases (CVDs). Recent studies show that deep learning models can be trained using labeled ECGs to achieve automatic detection of CVDs, assisting cardiologists in diagnosis. However, the deep learning models heavily rely on labels in training, while manual labeling is costly and time-consuming. This paper proposes a new self-supervised learning (SSL) method for multilead ECGs: bootstrap each lead's latent (BELL) to reduce the reliance and boost model performance in various tasks, especially when training data are insufficient.
BELL is a variant of the well-known bootstrap your own latent (BYOL). The BELL aims to learn prior knowledge from unlabeled ECGs by pretraining, benefitting downstream tasks. It leverages the characteristics of multilead ECGs. First, BELL uses the multiple-branch skeleton, which is more effective in processing multilead ECGs. Moreover, it proposes intra-lead and inter-lead mean square error (MSE) to guide pretraining, and their fusion can result in better performances. Additionally, BELL inherits the main advantage of the BYOL: No negative pair is used in pretraining, making it more efficient.
In most cases, BELL surpasses previous works in the experiments. More importantly, the pretraining improves model performances by 0.69% ∼ 8.89% in downstream tasks when only 10% of training data are available. Furthermore, BELL shows excellent adaptability to uncurated ECG data from a real-world hospital. Only slight performance degradation occurs (<1% in most cases) when using these data.
The results suggest that the BELL can alleviate the reliance on manual ECG labels from cardiologists, a critical bottleneck of the current deep learning-based models. In this way, the BELL can also help deep learning extend its application on automatic ECG analysis, reducing the cardiologists' burden in real-world diagnosis.
心电图（ECG）是心血管疾病（CVDs）最重要的诊断工具之一。最近的研究表明，深度学习模型可以使用标记的 ECG 进行训练，以实现 CVDs 的自动检测，辅助心脏病专家进行诊断。然而，深度学习模型在训练中严重依赖标签，而手动标记既昂贵又耗时。本文提出了一种新的多导联心电图自监督学习（SSL）方法：引导每个导联的潜在（BELL），以减少对标签的依赖，并提高模型在各种任务中的性能，尤其是在训练数据不足的情况下。
BELL 是一种广为人知的引导自身潜在（BYOL）的变体。BELL 的目的是通过预训练从未标记的 ECG 中学习先验知识，从而受益于下游任务。它利用了多导联 ECG 的特点。首先，BELL 使用多分支骨架，这在处理多导联 ECG 时更有效。此外，它提出了导联内和导联间均方误差（MSE）来指导预训练，并且它们的融合可以产生更好的性能。此外，BELL 继承了 BYOL 的主要优势：在预训练中不使用负对，使其更高效。
在大多数情况下，BELL 在实验中超过了之前的工作。更重要的是，当仅使用 10%的训练数据时，预训练可以将下游任务的模型性能提高 0.69%至 8.89%。此外，BELL 对来自真实医院的未经整理的 ECG 数据表现出出色的适应性。当使用这些数据时，性能下降幅度很小（大多数情况下小于 1%）。
结果表明，BELL 可以减轻对心脏病专家手动 ECG 标签的依赖，这是当前基于深度学习的模型的一个关键瓶颈。通过这种方式，BELL 还可以帮助深度学习将其应用扩展到自动 ECG 分析中，减轻心脏病专家在实际诊断中的负担。
