Introduction

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the developed world. Using photoplethysmography (PPG) and software algorithms, AF can be detected with high accuracy using smartphone camera-derived data. However, reports of diagnostic accuracy of standalone algorithms using wristband-derived PPG data are sparse, while this provides a means to perform long-term AF screening and monitoring. This study evaluated the diagnostic accuracy of a well-known standalone algorithm using wristband-derived PPG data.

Materials and Methods

Subjects recruited from a community senior care organization were instructed to wear the Wavelet PPG wristband on one arm and the Alivecor KardiaBand one-lead-ECG wristband on the other. Three consecutive measurements (duration per measurement: 60 s for PPG and 30 s for one-lead ECG) were performed with both devices, simultaneously. The PPG data were analyzed by the Fibricheck standalone algorithm and the ECG data by the Kardia algorithm. The results were compared to a reference standard (interpretation of the one-lead ECG by two independent cardiologists).

Results

A total of 180 PPGs and one-lead ECGs were recorded in 60 subjects, with a mean age of 70±17. AF was identified in 6 (10%) of the users, two users (3%) were not classifiable by the PPG algorithm and 1 user (2%) was not classifiable by the one-lead ECG algorithm. The diagnostic performance (sensitivity/specificity/positive predictive value/negative predictive value/accuracy) on user level was 100/96/75/100/97% for the PPG wristband and 100/98/86/100/98% for the one-lead ECG wristband.

Conclusions

In a small real-world cohort of elderly people, the standalone Fibricheck AF algorithm can accurately detect AF using Wavelet wristband-derived PPG data. Results are comparable to the Alivecor Kardia one-lead ECG device, with an acceptable unclassifiable/bad quality rate. This opens the door for long-term AF screening and monitoring.

中文翻译：

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评估用于检测腕带数据的心房颤动的独立光电容积描记术（PPG）算法的评估

介绍

心房颤动（AF）是发达国家中最常见的心律不齐。使用光电容积描记法（PPG）和软件算法，可以使用智能手机相机衍生的数据高精度地检测AF 。然而，使用腕带式PPG数据的独立算法的诊断准确性报告很少，而这提供了进行长期AF筛查和监测的手段。这项研究使用腕带衍生的PPG数据评估了一种著名的独立算法的诊断准确性。

材料和方法

从社区高级护理组织招募的受试者被指示一只手戴Wavelet PPG腕带，另一只手戴Alivecor KardiaBand单头ECG腕带。两种设备同时进行了三个连续的测量（每次测量的持续时间：PPG为60 s，单导联ECG为30 s）。PPG数据通过Fibricheck独立算法进行分析，ECG数据通过Kardia算法进行分析。将结果与参考标准（由两名独立的心脏病专家对单导联心电图的解释）进行比较。

结果

在60名受试者中共记录了180种PPG和一导ECG，平均年龄为70±17。在6（10％）个用户中发现了AF，PPG算法无法将其分类为两个用户（3％），单导ECG算法无法将其分类为1个用户（2％）。PPG腕带在用户水平上的诊断性能（敏感性/特异性/阳性预测值/阴性预测值/准确性）为100/96/75/100/97％，而一种为100/98/86/100/98％导联心电图腕带。

结论

在现实中的一小群老年人中，独立的Fibricheck AF算法可以使用Wavelet腕带式PPG数据准确检测AF。结果与Alivecor Kardia一导ECG设备相当，具有可接受的无法分类/不良质量率。这为长期AF筛查和监测打开了大门。