Photoplethysmography (PPG) is a non-invasive technology that measures changes
in blood volume in the microvascular bed of tissue. It is commonly used in
medical devices such as pulse oximeters and wrist worn heart rate monitors to
monitor cardiovascular hemodynamics. PPG allows for the assessment of
parameters (e.g., heart rate, pulse waveform, and peripheral perfusion) that
can indicate conditions such as vasoconstriction or vasodilation, and provides
information about microvascular blood flow, making it a valuable tool for
monitoring cardiovascular health. However, PPG is subject to a number of
sources of variations that can impact its accuracy and reliability, especially
when using a wearable device for continuous monitoring, such as motion
artifacts, skin pigmentation, and vasomotion. In this study, we extracted 27
statistical features from the PPG signal for training machine-learning models
based on gradient boosting (XGBoost and CatBoost) and Random Forest (RF)
algorithms to assess quality of PPG signals that were labeled as good or poor
quality. We used the PPG time series from a publicly available dataset and
evaluated the algorithm s performance using Sensitivity (Se), Positive
Predicted Value (PPV), and F1-score (F1) metrics. Our model achieved Se, PPV,
and F1-score of 94.4, 95.6, and 95.0 for XGBoost, 94.7, 95.9, and 95.3 for
CatBoost, and 93.7, 91.3 and 92.5 for RF, respectively. Our findings are
comparable to state-of-the-art reported in the literature but using a much
simpler model, indicating that ML models are promising for developing remote,
non-invasive, and continuous measurement devices.

本研究提取了 27 个统计特征，利用梯度提升（XGBoost 和 CatBoost）以及随机森林（RF）算法训练机器学习模型来评估血容量变化的 PPG 信号质量，在使用可穿戴设备进行连续监测时，考虑了运动伪影、皮肤色素沉着和血管运动等影响 PPG 准确性和可靠性的因素。研究发现，我们的模型在 XGBoost、CatBoost 和 RF 上分别达到了 Sensitivity（Se）、Positive Predicted Value（PPV）和 F1-score（F1）的 94.4、95.6、95.0，94.7、95.9、95.3 和 93.7、91.3、92.5，结果与文献中的最新成果相当，但使用了更简单的模型，证明机器学习模型在开发远程、非侵入性和连续测量设备方面具有很大的潜力。