Wireless Wearable Sensor Paired With Machine Learning for the Quantification of Tissue Oxygenation

The accurate knowledge of tissue oxygenation can be decisive for diagnostic applications in burns, limb injuries, and surgical interventions. Medical devices created for oxygenation measurements require extensive research and complex electronic, optical, and/or chemical techniques that typically result in non-mobile and expensive equipment. We have designed a wireless prototype that can detect changes in tissue oxygenation (pO(2)) making use of simple off-the-shelf electronic components and 3-D printing by measuring the phosphorescence intensity of an oxygen-sensing phosphor. The quantification of pO(2) was initially carried out by a phenomenological algorithm composed of a color-compensation matrix, a modified Stern-Volmer relation adding temperature dependence and an explicit photobleaching term. We improved the accuracy of measurement by employing a machine learning approach, which yields readings that are independent of changes in temperature and photobleaching, can be implemented into our data logging software, and potentially into the device's firmware.

Automated summary

This research presents a wireless prototype designed to detect changes in tissue oxygenation using off-the-shelf electronic components and 3D printing technology. The accuracy of oxygenation measurements has been improved through a machine learning approach, which can potentially be integrated into device firmware and software for stable and accurate readings.