引导每个导联的潜变量:多导联心电图自我监督学习的新方法

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

摘要

背景和目的: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带标签的心电图进行训练,从而实现心血管疾病的自动检测,协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于标签,而人工标注成本高且耗时。本文针对多导联心电图提出了一种新的自我监督学习(SSL)方法:引导每个导联的潜变量(BELL),以减少各种任务中的依赖并提高模型性能,尤其是在训练数据不足的情况下:BELL 是著名的自举潜迹法(BYOL)的一种变体。BELL旨在通过预训练从未标明的心电图中学习先验知识,从而使下游任务受益。它利用了多导联心电图的特点。首先,BELL 使用多分支骨架,这在处理多导联心电图时更为有效。此外,它还提出了导联内和导联间均方误差(MSE)来指导预训练,两者的融合能带来更好的性能。此外,BELL 还继承了 BYOL 的主要优点:预训练中不使用负对,因此效率更高:在大多数情况下,BELL 在实验中都超越了之前的研究成果。更重要的是,在下游任务中,当只有 10% 的训练数据可用时,预训练将模型性能提高了 0.69% ∼ 8.89%。此外,BELL 对来自真实世界医院的未经整理的心电图数据显示出极佳的适应性。只出现了轻微的性能下降(结论:BELL 可用于医院心电图数据:结果表明,BELL 可以减轻对心脏病专家手动心电图标签的依赖,而这正是当前基于深度学习的模型的一个关键瓶颈。这样,BELL 还能帮助深度学习扩展其在自动心电图分析方面的应用,减轻心脏病专家在实际诊断中的负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bootstrap each lead’s latent: A novel method for self-supervised learning of multilead electrocardiograms

Background and Objective:

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.

Method:

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.

Results:

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.

Conclusion:

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.
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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