Machine Learning in Arrhythmia and Electrophysiology.
Machine Learning in Arrhythmia and Electrophysiology.
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心律失常和电生理学中的机器学习。
DOI：
10.1161/circresaha.120.317872
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发表时间：
2021-02-19
影响因子：
20.1
通讯作者：
Shade JK
中科院分区：
文献类型：
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作者：
Trayanova NA;Popescu DM;Shade JK
机器学习（ML）是人工智能（AI）的一个分支，机器从大数据中学习，正处于席卷社会的技术变革浪潮的顶峰。心血管医学是许多机器学习应用的前沿，将它们纳入主流临床实践需要付出巨大的努力。在心脏电生理领域，ML的应用也得到了快速的增长和普及，特别是ML在心电图自动解释中的应用，这在文献中得到了广泛的报道。鲜为人知的是机器学习在心脏电生理和心律失常方面的其他应用，例如心律失常机制的基础科学研究，包括实验和计算；在发展更好的心电功能测绘技术；以及与心律失常管理相关的转化研究。在当前的审查中，我们全面审查这些机器学习应用，因为它们符合本期刊的范围。本综述分为三个部分。第一部分提供了一般ML原理和方法的概述，将为读者提供有关该主题的必要信息，作为邀请ML在心律失常研究中的进一步应用的基础。我们提供的基本信息可以作为一个如何设计和进行机器学习研究的指南。第二部分是对心律失常和电生理学研究的回顾，其中ML已被利用，突出了ML方法的广泛潜力。对于每个主题，我们全面概述了一般主题，同时回顾了该主题下利用ML的一些研究进展。最后，我们讨论了机器学习驱动的心脏电生理和心律失常研究的主要挑战和前景。
Machine learning (ML), a branch of artificial intelligence (AI), where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in three parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide how one might design and conduct a ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.
影响因子：
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作者：
Polak S;Wiśniowska B;Mendyk A;Pacławski A;Szlęk J
通讯作者：
Szlęk J
调节电压门控钾通道电压敏感性的残基的计算鉴定。
影响因子：
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作者：
Li B;Gallin WJ
通讯作者：
Gallin WJ