Blood Oxygen Saturation Estimation with Laser-Induced Graphene Respiration Sensor

Abstract

Measuring blood oxygen saturation (SpO₂) is crucial in a triage process for identifying patients with respiratory distress or shock, since low SpO₂ levels indicate compromised hemostability and the need for priority treatment. This paper explores the use of wearable mechanical deflection sensors based on laser-induced graphene (LIG) for SpO₂ estimation. The LIG sensors are attached to a subject’s chest for real-time monitoring of respiratory signals. We have developed a novel database of the respiratory signals, with corresponding SpO₂ values ranging from 86% to 100%. The database is used to develop an artificial neural network model for SpO₂ estimation. The neural network performance is promising, with regression metrics mean squared error = 0.184, mean absolute error = 0.301, root mean squared error = 0.429, and R-squared = 0.804. The use of mechanical respiration sensors in combination with neural networks in biosensing opens new possibilities for noninvasive SpO₂ monitoring and other innovative applications.