Accuracy enhancement in reflective pulse oximetry by considering wavelength-dependent pathlengths.
Idoia BadiolaVladimir BlazekV Jagadeesh KumarBoby GeorgeSteffen LeonhardtChristoph Hoog AntinkPublished in: Physiological measurement (2022)
Objective. Noninvasive measurement of oxygen saturation ( SpO 2 ) using transmissive photoplethysmography (tPPG) is clinically accepted and widely employed. However, reflective photoplethysmography (rPPG)-currently present in smartwatches-has not become equally accepted, partially because the pathlengths of the red and infrared PPGs are patient-dependent. Thus, even the most popular 'Ratio of Modulation' ( R ) method requires patient-dependent calibration to reduce the errors in the measurement of SpO 2 using rPPGs. Approach. In this paper, a correction factor or 'pathlength ratio' β is introduced in an existing calibration-free algorithm that compensates the patient-dependent pathlength variations, and improved accuracy is obtained in the measurement of SpO 2 using rPPGs. The proposed pathlength ratio β is derived through the analytical model of a rPPG signal. Using the new expression and data obtained from a human hypoxia study wherein arterial oxygen saturation values acquired through Blood Gas Analysis were employed as a reference, β is determined. Main results. The results of the analysis show that a specific combination of the β and the measurements on the pulsating part of the natural logarithm of the red and infrared PPG signals yields a reduced root-mean-square error (RMSE). It is shown that the average RMSE in measuring SpO 2 values reduces to 1 %. Significance. The human hypoxia study data used for this work, obtained in a previous study, covers SpO 2 values in the range from 70 % to 100 %, and thus shows that the pathlength ratio β proposed here works well in the range of clinical interest. This work demonstrates that the calibration-free method applicable for transmission type PPGs can be extended to determine SpO 2 using reflective PPGs with the incorporation of the correction factor β . Our algorithm significantly reduces the number of parameters needed for the estimation, while keeping the RMSE below the clinically accepted 2 %.