Paper Info
Title
Measuring Oxygen Saturation with Smartphone Cameras using Convolutional Neural Networks.
Abstract
Arterial oxygen saturation (SaO2) is an indicator of how much oxygen is carried by hemoglobin in the blood. Having enough oxygen is vital for the functioning of cells in the human body. Measurement of SaO2 is typically estimated with a pulse oximeter, but recent works have investigated how smartphone cameras can be used to infer SaO2. In this paper, we propose an innovate method for the measurement of SaO2 with a smartphone using convolutional neural networks and preprocessing steps to better guard against motion artifacts. To evaluate this methodology, we conducted a breath-holding study involving 39 participants. We compare the results using two different mobile phones. We compare our model with the ratio of ratios model that is widely used in pulse oximeter applications, showing that our system has significantly lower mean absolute error (2.02%) from a medical pulse oximeter.
Year
DOI
Venue
2019
10.1109/JBHI.2018.2887209
IEEE journal of biomedical and health informatics
Keywords
Field
DocType
Biomedical measurement,Cameras,Blood,Mobile handsets,Sensors,Atmospheric measurements,Particle measurements
Computer vision,Pattern recognition,Convolutional neural network,Computer science,Oxygen saturation,Mean absolute error,Pulse (signal processing),Preprocessor,Artificial intelligence,Atmospheric measurements
Journal
Volume
Issue
ISSN
23
6
2168-2208
Citations 
PageRank 
References 
1
0.36
0
Authors
3
Name
Order
Citations
PageRank
Xinyi Ding141.44
Damoun Nassehi210.36
Eric C. Larson358330.46