  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.
