Model Explainability in Physiological and Healthcare-based Neural Networks

Abstract

The estimation and monitoring of SpO2 are crucial for assessing lung function and treating chronic pulmonary diseases. The COVID-19 pandemic has highlighted the importance of early detection of changes in SpO2, particularly in asymptomatic patients with clinical deterioration. However, conventional SpO2 measurement methods rely on contact-based sensing, presenting the risk of cross-contamination and complications in patients with impaired limb perfusion. Additionally, pulse oximeters may not be available in marginalized communities and undeveloped countries. To address these limitations and provide a more comfortable and unobtrusive way to monitor SpO2, recent studies have investigated SpO2 measurement using videos. However, measuring SpO2 using cameras in a contactless way, particularly from smartphones, is challenging due to weaker physiological signals and lower optical selectivity of smartphone camera sensors. The system includes three main steps: 1) extraction of the region of interest (ROI), which includes the palm and back of the hand, from the smartphone-captured videos; 2) spatial averaging of the ROI to produce R, G, and B time series; and 3) feeding the time series into an optophysiology-inspired CNN for SpO2 estimation. Our proposed method can provide a more efficient and accurate way to monitor SpO2 using videos captured from consumer-grade smartphones, which can be especially useful in telehealth and health screening settings.