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Learning Blood Oxygen from Respiration Signals

Monitoring blood oxygen is critical in a variety of medical conditions including COVID-19. For almost a century, pulse oximetry has been the only noninvasive method for measuring blood oxygen. While highly useful, pulse oximetry has important limitations. It requires wearable sensors, which can be cumbersome for older patients. It is also known to be biased when used for dark-skinned subjects. In this paper, we demonstrate, for the first time, the feasibility of predicting oxygen saturation from breathing. By eliminating dependency on oximetry, we eliminate bias against skin color. Further, since breathing can be monitored without body contact by analyzing the radio signal in the environment, we show that oxygen too can be monitored without any wearable devices. We introduce a new approach for leveraging auxiliary variables and tasks via a multi-headed neural network model. Empirical results show that our model achieves good accuracy on existing medical datasets and can potentially be used to monitor the recovery of COVID-19 patients.

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