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基于机器学习的分类算法的外部验证,用于使用智能手机光电显微镜的心脏转心心动脉动患者的门诊心律诊断:SMARTBEATS-ALGO研究

Jonatan Fernstad^1,2, Emma Svennberg³, Peter Åberg¹

¹Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology

|February 17, 2025

概括

一个机器学习算法使用智能手机光电显微镜 (PPG) 在无人监督的环境中准确诊断了心房动 (AF) 和心房动 (AFL). 这证实了心律监测的非侵入性方法.

科学领域:

心脏病学心脏病学
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

心房动 (AF) 和心房动 (AFL) 是常见的心律失常症.
准确的诊断和监测对于有效的管理至关重要.
当前的诊断方法可能需要专门的设备或亲自访问.

研究的目的:

为外部验证用于诊断心律障碍的机器学习 (ML) 算法.
通过智能手机光电显微镜 (PPG) 数据评估算法的性能.
为了评估算法在无监督的,在心脏转换患者的门诊环境.

主要方法:

使用支持向量机器开发了一个ML算法,用于从PPG信号分类心律.
为AF或AFL进行心脏转移的患者使用iPhone 7 (CORAI心脏监视器) 与心电图 (KardiaMobile) 同时记录了PPG.
该算法在一个单独的队列上进行了训练,并在280名患者的18005条记录上进行了验证.

主要成果:

该算法在外部验证队列中实现了高诊断性能.
对于AF检测,灵敏度,特异性和准确性为99.7%.
对于AF/AFL检测,灵敏度,特异性和准确性分别为99.3%,99.1%和99.2%.

结论:

这种基于ML的算法在智能手机PPG的AF和AFL诊断方面表现出色.
这项技术可以最大限度地减少手动审查和心电图验证的需要.
它提供了一个有前途的,非侵入性的工具,用于在门诊环境中监测心律.

关键词:

心房动是一种心房动.在心房动.机器学习是机器学习.摄影电解质量学摄影电解质量学一个智能手机的智能手机.远程监控远程监控

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