Pulse Rate Guided Oxygen Saturation Monitoring Using a Wearable Armband Sensor

Continuous clinical grade measurement of $SpO_{2}$ in out-of-hospital settings remains a challenge despite the widespread use of photoplethysmography (PPG) based wearable devices for health and wellness applications. This article presents two $SpO_{2}$ algorithms: PRR (pulse rate derived ratio-of-ratios) and GPDR (green-assisted peak detection ratio-of-ratios), that utilize unique pulse rate frequency estimations to isolate the pulsatile (AC) component of red and infrared PPG signals and derive $SpO_{2}$ measurements. The performance of the proposed $SpO_{2}$ algorithms are evaluated using an upper-arm wearable device derived green, red, and infrared PPG signals, recorded in both controlled laboratory settings involving healthy subjects $(n=36)$ and an uncontrolled clinic application involving COVID-19 patients $(n=52)$ . GPDR exhibits the lowest root mean square error (RMSE) of $1.6\pm 0.6\%$ for a respiratory exercise test, $3.6 \pm 1.0\%$ for a standard hypoxia test, and $2.2\pm 1.3\%$ for an uncontrolled clinic use-case. In contrast, PRR provides relatively higher error $(P> 0.05)$ but with greater coverage overall. Mean error across all combined datasets were $0.2\pm 2.8\%$ and $0.3\pm 2.4\%$ for PRR and GPDR respectively. Both $SpO_2$ algorithms achieve great performance of low error with high coverage on both uncontrolled clinic and controlled laboratory conditions.