A PPG sensor-based multi-feature machine learning approach for wide-range and high-precision S_pO₂measurement

This study aimed to improve peripheral oxygen saturation (SpO2) estimation across a wide range for hypoxemic-risk assessment in chronic obstructive pulmonary disease (COPD) using dual-wavelength photoplethysmography (PPG) and machine learning. Approach. A MAX30102-based module acquired synchronized red (660 nm) and near-infrared (880 nm) PPG signals from the right index finger. A CMS50DA oximeter on the right middle finger provided concurrent reference SpO2 readings for training and evaluation. Smartphone video captured its display for manual label extraction and independent checking. The ratio-of-ratios (R) and morphological, temporal, statistical, and derivative features were extracted. Principal component analysis-based feature selection identified a feature subset. Machine-learning and PPG-oriented deep-learning models were compared with conventional R-ratio calibration using mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2). Main results. The Extra Trees regressor achieved the lowest RMSE among evaluated models, with an MAE of 0.38 percentage points, an RMSE of 0.90 percentage points, and an R2 of 0.91. It outperformed conventional R-ratio calibration. In the hypoxemic range (70% ≤ SpO2 ≤ 90%), MAE was 0.98 percentage points, RMSE was 2.04 percentage points, and R2 was 0.66.

Combining dual-wavelength PPG with multi-domain features and machine learning shows potential for wide-range SpO2 estimation and hypoxemic-risk assessment in COPD.