Machine learning nominal max oxygen consumption from wearable reflective pulse oximetry with density functional theory

Abstract

AbstractWearable sensors have revolutionized modern health towards precision medicine. Many measurements taken by wearables are not currently used for medical purposes, and their physical roots are not understood. Here we show the origin of inferring oxygen saturation from measuring estimated oxygen variation while preparing for a marathon via spectrophotometric pulsatile blood flow transcutaneous reflectance oximetry, building six years of data tracking the stages of running a marathon. Based on inputs from quantum mechanical simulations into classical wave and electromagnetic theory models, the imaginary part of the complex dielectric function of the active site of human hemoglobin is altered upon oxygen binding to the heme group, changing the extinction coefficient and selectively absorbing wavelengths of light in a way that enables its detection from the ratio of red to infrared absorbance. A fundamental nominal max oxygen consumption is inferred from quantum mechanical absorbance spectra with and without oxygen binding by training a machine learning model on 1.5 years of daily wearable reflective pulse oximetry data with an R2of 0.84. Reflectance oximetry is strongly correlated (R2of 0.96) to standard pulse oximetry, such as through earlobe or fingertip, enabling accurate, rapid, regular, and automated monitoring of vital signs with wearable sensors on smart watches and fitness trackers, and supplying artificial intelligence inferences of function-symptom relationships for precision medicine.