光电容积描记术(PPG)信号质量作为心率(HR)测量准确性的代理在各种公共卫生环境中很有用。从短期临床诊断到为公共卫生政策提供信息的自由生活健康行为监测研究。每个上下文对可接受的信号质量具有不同的容限,期望一个单一的阈值来满足所有上下文的需求是简化的。在这项研究中,我们提出了两种不同的指标作为PPG信号质量的滑动量表,并评估了与地面实况心电图(ECG)测量结果相比,它们与HR测量结果准确性的关联.
方法:我们使用了两个公开可用的PPG数据集(BUTPPG和Troika)来测试我们的信号质量指标是否可以识别出与黄金标准视觉检查相比较差的信号质量。为了帮助解释滑动尺度指标,我们使用ROC曲线和Kappa值来计算指南切点并评估一致性,分别。然后,我们使用Troika数据集和从胸部收集的PPG数据的原始数据集来检查信号质量的连续度量与HR准确性之间的关联。使用平均绝对误差(MAE)和均方根误差(RMSE)将基于PPG的HR估计值与参考HR估计值进行比较。点双材料相关性用于检查二进制信号质量与HR误差度量(MAE和RMSE)之间的关联。
结果:来自BUTPPG数据的ROC分析显示,STD宽度的信号质量指标的AUC为0.758(95%CI0.624至0.892),自我一致性的AUC为0.741(95%CI0.589至0.883)。在三驾马车和最初收集的数据中,标准不良信号质量与信号质量度量之间存在显着相关性。信号质量与HR准确性高度相关(MAE和RMSE,分别)在PPG和地面实况心电图之间。
结论:这项概念验证工作证明了一种评估信号质量的有效方法,并证明了不良信号质量对HR测量的影响。我们的连续信号质量指标允许估计其他紧急指标中的不确定性,例如依赖于多个独立生物识别技术的能量消耗。这种开源方法增加了我们工作在公共卫生环境中的可用性和适用性。
Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement.
METHODS: We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE).
RESULTS: ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG.
CONCLUSIONS: This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.