Open AccessRobust non-contact peripheral oxygenation saturation measurement using smartphone-enabled imaging photoplethysmography
Author(s) -
ZhangHua Sun,
Q. Peter He,
Yuandong Li,
Ke Wang,
Ke Wang
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.419268
Subject(s) - photoplethysmogram , computer science , rgb color model , pulse oximetry , oxygen saturation , computer vision , artificial intelligence , biomedical engineering , medicine , chemistry , organic chemistry , filter (signal processing) , anesthesia , oxygen
We propose a robust non-contact method to accurately estimate peripheral oxygen saturation (SpO 2 ) using a smartphone-based imaging photoplethysmography. The method utilizes the built-in color camera as a remote sensor and the built-in flashlight as illumination to estimate the SpO 2 . Following the ratio of ratios between green and red channels, we introduce a multiple linear regression algorithm to improve the SpO 2 estimation. The algorithm considers the ratio of ratios and the reflectance images recorded at the RGB channels during a calibration process to obtain a set of weighting coefficients to weigh each contributor to the final determination of SpO 2 . We demonstrate the proposed smartphone-based method of estimating the SpO 2 on five healthy volunteers whose arms are conditioned by a manual pressure cuff to manipulate the SpO 2 between 90∼100% as detected simultaneously by a medical-grade pulse oximeter. Experimental results indicate that the overall estimated error between the smartphone and the reference pulse oximeter is 0.029 ± 1.141%, leading to a 43% improvement over the conventional ratio of ratios method (0.008 ± 2.008%). In addition, the data sampling time in the current method is 2 seconds, similar to the sampling cycle used in the commercial medical-grade pulse oximeters.