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Photoplethysmography (PPG) is a non-invasive technology that measures changesin blood volume in the microvascular bed of tissue. It is commonly used inmedical devices such as pulse oximeters and wrist worn heart rate monitors tomonitor cardiovascular hemodynamics. PPG allows for the assessment ofparameters (e.g., heart rate, pulse waveform, and peripheral perfusion) thatcan indicate conditions such as vasoconstriction or vasodilation, and providesinformation about microvascular blood flow, making it a valuable tool formonitoring cardiovascular health. However, PPG is subject to a number ofsources of variations that can impact its accuracy and reliability, especiallywhen using a wearable device for continuous monitoring, such as motionartifacts, skin pigmentation, and vasomotion. In this study, we extracted 27statistical features from the PPG signal for training machine-learning modelsbased on gradient boosting (XGBoost and CatBoost) and Random Forest (RF)algorithms to assess quality of PPG signals that were labeled as good or poorquality. We used the PPG time series from a publicly available dataset andevaluated the algorithm s performance using Sensitivity (Se), PositivePredicted 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 forCatBoost, and 93.7, 91.3 and 92.5 for RF, respectively. Our findings arecomparable to state-of-the-art reported in the literature but using a muchsimpler model, indicating that ML models are promising for developing remote,non-invasive, and continuous measurement devices.

Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices