背景:心电图(ECG)是最常见的非侵入性诊断工具之一,可以提供有关患者健康状况的有用信息。深度学习(DL)是一个激烈的探索领域,在大多数基于生理信号创建强大诊断模型的尝试中处于领先地位。
目的:本研究旨在对应用于各种临床应用的ECG数据的DL方法进行系统综述。
方法:通过结合“深度学习”和“ecg”等关键字,系统地搜索了PubMed搜索引擎\"\"ekg,\"\"心电图,\"\"心电图,“和”心电图。“在筛选标题和摘要后,不相关的文章被排除在研究之外,其余文章进行了进一步审查。排除文章的原因是用英语以外的任何语言编写的手稿,没有ECG数据或DL方法参与研究,并且缺乏对拟议方法的定量评估。
结果:我们确定了2020年1月至2021年12月之间发表的230篇相关文章,并将其分为6种不同的医学应用。即,血压估计,心血管疾病诊断,心电图分析,生物识别,睡眠分析,和其他临床分析。我们为每个应用领域提供了最先进的DL策略的完整说明,以及主要的ECG数据源。我们还提出了开放的研究问题,例如缺乏解决训练数据集中血压变异性问题的尝试,并指出DL模型设计和实施中的潜在差距。
结论:我们希望这篇综述将为应用于ECG数据的最先进的DL方法提供见解,并指出DL研究的未来方向,以创建可以帮助医学专家进行临床决策的强大模型。
BACKGROUND: Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient\'s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals.
OBJECTIVE: This study aimed to provide a systematic
review of DL methods applied to ECG data for various clinical applications.
METHODS: The PubMed search engine was systematically searched by combining \"deep learning\" and keywords such as \"ecg,\" \"ekg,\" \"electrocardiogram,\" \"electrocardiography,\" and \"electrocardiology.\" Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches.
RESULTS: We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models.
CONCLUSIONS: We expect that this
review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.