Fernstad Jonatan, Svennberg Emma, Åberg Peter, Kemp Gudmundsdottir Katrin, Jansson Anders, Engdahl Johan
Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden.
Department of Cardiology, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden.
Europace. 2025 Mar 28;27(4). doi: 10.1093/europace/euaf031.
The aim of this study was to perform an external validation of an automatic machine learning (ML) algorithm for heart rhythm diagnostics using smartphone photoplethysmography (PPG) recorded by patients with atrial fibrillation (AF) and atrial flutter (AFL) pericardioversion in an unsupervised ambulatory setting.
Patients undergoing cardioversion for AF or AFL performed 1-min heart rhythm recordings pericardioversion at least twice daily for 4-6 weeks, using an iPhone 7 smartphone running a PPG application (CORAI Heart Monitor) simultaneously with a single-lead electrocardiogram (ECG) recording (KardiaMobile). The algorithm uses support vector machines to classify heart rhythm from smartphone-PPG. The algorithm was trained on PPG recordings made by patients in a separate cardioversion cohort. Photoplethysmography recordings in the external validation cohort were analysed by the algorithm. Diagnostic performance was calculated by comparing the heart rhythm classification output to the diagnosis from the simultaneous ECG recordings (gold standard). In total, 460 patients performed 34 097 simultaneous PPG and ECG recordings, divided into 180 patients with 16 092 recordings in the training cohort and 280 patients with 18 005 recordings in the external validation cohort. Algorithmic classification of the PPG recordings in the external validation cohort diagnosed AF with sensitivity, specificity, and accuracy of 99.7%, 99.7% and 99.7%, respectively, and AF/AFL with sensitivity, specificity, and accuracy of 99.3%, 99.1% and 99.2%, respectively.
A machine learning-based algorithm demonstrated excellent performance in diagnosing atrial fibrillation and atrial flutter from smartphone-PPG recordings in an unsupervised ambulatory setting, minimizing the need for manual review and ECG verification, in elderly cardioversion populations.
Clinicaltrials.gov, NCT04300270.
本研究旨在对一种自动机器学习（ML）算法进行外部验证，该算法用于在无监督的动态环境中，根据心房颤动（AF）和心房扑动（AFL）患者在心脏复律期间使用智能手机记录的光电容积脉搏波描记图（PPG）进行心律诊断。
接受AF或AFL心脏复律的患者，使用运行PPG应用程序（CORAI心脏监测器）的iPhone 7智能手机，在心脏复律期间每天至少进行两次1分钟的心律记录，持续4至6周，同时进行单导联心电图（ECG）记录（KardiaMobile）。该算法使用支持向量机从智能手机PPG中对心律进行分类。该算法在一个单独的心脏复律队列中患者的PPG记录上进行训练。通过该算法分析外部验证队列中的光电容积脉搏波描记图记录。通过将心律分类输出与同步ECG记录的诊断结果（金标准）进行比较来计算诊断性能。共有460名患者进行了34097次同步PPG和ECG记录，分为训练队列中的180名患者有16092次记录，以及外部验证队列中的280名患者有18005次记录。外部验证队列中PPG记录的算法分类诊断AF的敏感性、特异性和准确性分别为99.7%、99.7%和99.7%，诊断AF/AFL的敏感性、特异性和准确性分别为99.3%、99.1%和99.2%。
一种基于机器学习的算法在无监督的动态环境中，根据智能手机PPG记录诊断心房颤动和心房扑动方面表现出优异的性能，在老年心脏复律人群中最大限度地减少了人工审查和ECG验证的需求。
Clinicaltrials.gov，NCT04300270。
