Abstract
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.