在手腕设备上对机器学习连续脉冲率算法的多中心评估
Weixuan Chen1, Rafael Cordero2, Jessie Lever Taylor1
1Empatica Inc., Cambridge, MA, USA.
Digital biomarkers
|December 13, 2024
概括
这项研究验证了用于连续脉冲率 (PR) 监测的手腕穿戴的光电显微镜 (PPG) 传感器和机器学习 (ML) 算法. 这项技术甚至在运动中实现了临床级准确性,使其适合广泛使用.
科学领域:
- 生物医学工程 生物医学工程
- 数字健康技术 数字健康技术
- 心血管监测 心血管监测
背景情况:
- 佩戴在手腕上的光电显微镜 (PPG) 传感器对于持续监测心律至关重要.
- 手腕PPG信号易受运动工件的影响,影响准确性.
- 机器学习 (ML) 算法可以增强脉冲率 (PR) 追踪,但在临床使用中面临监管障碍.
研究的目的:
- 用手腕佩戴的PPG传感器和用于连续PR测量的ML算法来评估数字健康技术的准确性.
- 评估设备在各种条件下的性能,包括运动和不同的人口群体.
主要方法:
- 三项独立的临床试验招募了志愿者,与研究设备和FDA批准的心电图 (ECG) 同时进行监测.
- 协议模拟了现实活动,以捕捉无运动和运动条件下的性能.
- 精度指标包括平方根平均值 (ARMS),偏差,平均绝对误差 (MAE),平均绝对百分比误差 (MAPE) 和一致性相关系数 (CCC).
主要成果:
- 该设备在静止状态下达到1.67bpm的精度,在运动状态下达到4.39bpm,达到临床接受值.
- 偏差和约定极限 (LoA) 在两种条件下都处于可接受范围之内 (无运动: -0.09 bpm, -3.36至3.17 bpm;运动:0.51 bpm, -8.05至9.06 bpm).
- 高一致性相关系数 (和CCC>0.98) 表示与ECG参考非常一致,显示了各子组的概括性.
结论:
- 基于ML的连续PR估计表明了临床级准确性和通用性.
- 该技术有效地减轻了身体运动,健康状况和人口统计学等混因素.
- 这证实了该设备适用于受益于持续PR监测的人群,支持其用于追溯审查和趋势分析的临床实用性.
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