Department of Exercise Science, University of South Carolina, Columbia, SC 29208, USA.
College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA.
Sensors (Basel). 2023 Mar 24;23(7):3429. doi: 10.3390/s23073429.
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.
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).
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.
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.
目的：光电容积脉搏波描记术（PPG）信号质量可作为心率（HR）测量准确性的替代指标，在从短期临床诊断到告知公共卫生政策的自由生活健康行为监测研究等各种公共卫生环境中都很有用。每个环境对可接受的信号质量都有不同的容忍度，期望单一的阈值能够满足所有环境的需求是过于简化的。在这项研究中，我们提出了两种不同的指标作为 PPG 信号质量的滑动尺度，并评估了它们与基于地面实况心电图（ECG）测量的 HR 测量准确性的关联。
方法：我们使用了两个公开的 PPG 数据集（BUT PPG 和 Troika）来测试我们的信号质量指标是否可以与黄金标准视觉检查相比识别出较差的信号质量。为了帮助解释滑动尺度指标，我们分别使用 ROC 曲线和 Kappa 值来计算指南截止点和评估一致性。然后，我们使用 Troika 数据集和从胸部采集的原始 PPG 数据集，研究信号质量的连续指标与 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）比较基于 PPG 的 HR 估计值与参考 HR 估计值。点二项式相关系数用于检验二进制信号质量与 HR 误差指标（MAE 和 RMSE）之间的关联。
结果：来自 BUT PPG 数据的 ROC 分析显示，用于 STD-width 的信号质量指标的 AUC 为 0.758（95%CI 0.624 至 0.892），用于自一致性的信号质量指标为 0.741（95%CI 0.589 至 0.883）。在 Troika 和原始采集的数据中，均存在标准差信号质量差与信号质量指标之间的显著相关性。信号质量与 PPG 和地面实况 ECG 之间的 HR 准确性高度相关（分别为 MAE 和 RMSE）。
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，并展示了较差的信号质量对 HR 测量的影响。我们的连续信号质量指标可以估计其他新兴指标的不确定性，例如依赖于多个独立生物特征的能量消耗。这种开源方法增加了我们在公共卫生环境中的工作的可用性和适用性。
