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.

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