Funston Rebecca, Gibbs Austin, Diven Jordan, Francey Jonathan, Easlea Holly, Murray Stacey, Fitzpatrick Matthew, Condon Adrian, Mitchell Andrew R J
B-Secur, Queen's Road, Belfast BT3 9DT, UK.
The Allan Lab, Jersey General Hospital, St. Helier, Jersey.
J Electrocardiol. 2022 Sep-Oct;74:88-93. doi: 10.1016/j.jelectrocard.2022.08.004. Epub 2022 Aug 27.
Technological advances have led to electrocardiograph (ECG) functionality becoming increasingly accessible in wearable health devices, which has the potential to vastly expand the clinician's ability to monitor, diagnose, and manage cardiac health conditions. However, achieving the high signal quality necessary to make an accurate and confident diagnosis is inherently challenging on consumer device-acquired ECGs. Effective signal conditioning is crucial to make ECG data from wearable devices clinically actionable.
This study evaluates the heart rate (HR) performance of ECG data collected on the HeartKey® Test Watch, a single lead, dry electrode wrist wearable, against data acquired on two criterion devices: the Bittium® Faros 180, a gold standard wet electrode ambulatory monitoring device, and the HeartKey Chest Module.
ECG data was simultaneously acquired on three devices during a multi-stage protocol (sitting, walking, standing) designed to reflect the motion noise of real-life scenarios. Raw ECGs from the HeartKey Test Watch and HeartKey Chest Module were processed through HeartKey software, and the accuracy of the outputted heart rate data was compared to that of the criterion device at each stage of the protocol. A beat rejection analysis was performed to provide insight into the degree of high-frequency noise present in ECGs recorded on the HeartKey Test Watch.
Data acquired on the HeartKey Test Watch and processed by HeartKey software generated HR metrics that closely matched that of the criterion devices throughout the protocol. Bland-Altman analysis showed a mean absolute HR difference of 0.74, 1.21, 0.80 bpm during the sitting, walking, and standing stages respectively, which is within the ± 10% or ±5 bpm range required by ANSI EC13. ECG data from the HeartKey Test Watch had a higher beat rejection rate relative to the HeartKey Chest Module (8.5% vs ∼0%) due to the excessive high-frequency noise generated during the motion-based protocol.
HeartKey software demonstrated highly accurate HR performance, comparable to that of the criterion Faros device, when processing challenging ECG data acquired on a single lead, dry electrode wrist wearable during both non-motion and motion-based protocols.
技术进步使得心电图（ECG）功能在可穿戴健康设备中越来越普及，这有可能极大地扩展临床医生监测、诊断和管理心脏健康状况的能力。然而，在消费设备采集的心电图上获得准确可靠诊断所需的高信号质量本质上具有挑战性。有效的信号调理对于使可穿戴设备的心电图数据具有临床可操作性至关重要。
本研究评估了在HeartKey®测试手表（一种单导联、干电极腕戴式设备）上采集的心电图数据的心率（HR）性能，并与在两种标准设备上采集的数据进行比较：Bittium®Faros 180（一种金标准湿电极动态监测设备）和HeartKey胸部模块。
在一个旨在反映现实生活场景运动噪声的多阶段方案（坐着、行走、站立）中，同时在三种设备上采集心电图数据。来自HeartKey测试手表和HeartKey胸部模块的原始心电图通过HeartKey软件进行处理，并将输出心率数据的准确性与方案各阶段的标准设备进行比较。进行了心跳剔除分析，以深入了解在HeartKey测试手表上记录的心电图中存在的高频噪声程度。
在整个方案中，在HeartKey测试手表上采集并由HeartKey软件处理的数据生成的心率指标与标准设备的心率指标密切匹配。Bland-Altman分析显示，在坐着、行走和站立阶段，平均绝对心率差异分别为0.74、1.21、0.80次/分钟，在ANSI EC13要求的±10%或±5次/分钟范围内。由于基于运动的方案中产生的过高高频噪声，来自HeartKey测试手表的心电图数据相对于HeartKey胸部模块具有更高的心跳剔除率（8.5%对~0%）。
在处理在非运动和基于运动的方案中在单导联、干电极腕戴式设备上采集的具有挑战性的心电图数据时，HeartKey软件表现出高度准确的心率性能，与标准Faros设备相当。
