Non-Contact SpO2 Prediction System Based on a Digital Camera

Ali Al-Naji,Jinan F Mahdi,Ghaidaa A Khalid,Javaan Chahl

doi:10.3390/app11094255

Abstract

Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware which may cause inconvenience and involve additional appointments with the patient. This study proposes a computer vision-based system using a digital camera to measure SpO2 on the basis of the imaging photoplethysmography (iPPG) signal extracted from the human’s forehead without the need for restricting the subject or physical contact. The proposed camera-based system decomposes the iPPG obtained from the red and green channels into different signals with different frequencies using a signal decomposition technique based on a complete Ensemble Empirical Mode Decomposition (EEMD) technique and Independent Component Analysis (ICA) technique to obtain the optical properties from these wavelengths and frequency channels. The proposed system is convenient, contactless, safe and cost-effective. The preliminary results for 70 videos obtained from 14 subjects of different ages and with different skin tones showed that the red and green wavelengths could be used to estimate SpO2 with good agreement and low error ratio compared to the gold standard of pulse oximetry (SA210) with a fixed measurement position.

Highlights

Eighteen months after the emergence of COVID-19, the number of cases worldwide amounts to more than 152 million people, with more than 3.2 million deaths
Pulse oximetry is a non-invasive optical technique based on a photoplethysmography (PPG) signal obtained from the wavelength illumination transmitted or reflected by a tissue that is used to measure blood oxygen saturation (SpO2) level in both hospital and home environments [4]
The principal contributions of the proposed system are: (1) the possibility of predicting SpO2 based on ratio of ratios’ (RRs) obtained from a visible-light camera using green wavelength instead of IR while keeping red wavelength as the saturation-sensitive wavelength, (2) the extraction of SpO2 directly from one camera using two channel components (R and G) and reducing skin tone limitations resulting from the B channel, improving the accuracy of SpO2 measurement, (3) the automatic selection of the forehead region based on a skin detection algorithm and the use of a signal decomposition technique based on complete Ensemble Empirical Mode Decomposition (EEMD) [38] and Independent Component Analysis (ICA) [39] techniques to decompose imaging photoplethysmography (iPPG) signals into different frequency signals and to remove noise artefacts embedded in the iPPG signal

Summary

Introduction

Eighteen months after the emergence of COVID-19, the number of cases worldwide amounts to more than 152 million people, with more than 3.2 million deaths. Pulse oximetry is a non-invasive optical technique based on a photoplethysmography (PPG) signal obtained from the wavelength illumination transmitted or reflected by a tissue that is used to measure blood oxygen saturation (SpO2) level in both hospital and home environments [4].

Results

Conclusion