摘要:In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO
2) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO
2, we attempted to demonstrate accurate SpO
2 estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO
2 from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO
2 was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPG
green-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO
2. The accurate SpO
2 estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.