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Accuracy enhancement in reflective pulse oximetry by considering wavelength-dependent pathlengths
Idoia Badiola1, Vladimir Blazek1,2, V Jagadeesh Kumar3
1Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52064 Aachen, Germany.
Abstract:
Objective.Noninvasive measurement of oxygen saturation (SpO2) 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 ofSpO2using rPPGs.Approach.In this paper, a correction factor or 'pathlength ratio'βis introduced in an existing calibration-free algorithm that compensates the patient-dependent pathlength variations, and improved accuracy is obtained in the measurement ofSpO2using rPPGs. The proposed pathlength ratioβis 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,βis determined.Main results.The results of the analysis show that a specific combination of theβand 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 measuringSpO2values reduces to 1 %.Significance.The human hypoxia study data used for this work, obtained in a previous study, coversSpO2values in the range from 70 % to 100 %, and thus shows that the pathlength ratioβproposed 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 determineSpO2using reflective PPGs with the incorporation of the correction factorβ. Our algorithm significantly reduces the number of parameters needed for the estimation, while keeping the RMSE below the clinically accepted 2 %.
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