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.

翻译：光电容积脉搏波（PPG）是一种非侵入性技术，用于测量组织微血管床中血容量的变化。它常用于脉搏血氧仪和腕式心率监测仪等医疗设备中，以监测心血管血流动力学。PPG能够评估心率、脉搏波形和外周灌注等参数，这些参数可指示血管收缩或扩张等状况，并提供有关微血管血流的信息，使其成为监测心血管健康的宝贵工具。然而，PPG受到多种变异源的影响，尤其在使用可穿戴设备进行连续监测时，其准确性和可靠性可能受到运动伪影、皮肤色素沉着和血管运动等因素的干扰。在本研究中，我们从PPG信号中提取了27个统计特征，用于训练基于梯度提升（XGBoost和CatBoost）和随机森林（RF）算法的机器学习模型，以评估标记为“良好”或“较差”质量的PPG信号。我们使用公开数据集中的PPG时间序列，并通过敏感性、阳性预测值（PPV）和F1分数对算法性能进行评估。我们的模型在XGBoost上实现了94.4、95.6和95.0的Se、PPV和F1分数，在CatBoost上实现了94.7、95.9和95.3，在RF上实现了93.7、91.3和92.5。我们的结果与文献中报道的最新成果相当，但使用了更简单的模型，这表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广阔前景。