Non-contact methods can expand the application scenarios of blood oxygen measurement with better hygiene and comfort, but the traditional non-contact methods are usually less accurate. In this study a novel non-contact approach for measuring peripheral oxygen saturation (SpO2) using deep learning and near-infrared multispectral videos is proposed. After a series of data processing including shading correction, global detrending and spectral channel normalization to reduce the influences from illumination non-uniformity, ambient light, and skin tone, the preprocessed video data are split into half-second clips (30 frames) as input of the 3D convolutional residual network. In the experiment, multispectral videos in 25 channels of hand palms from 7 participants were captured. The experimental results show that the proposed approach can accurately estimate SpO2 from near-infrared multispectral videos, which demonstrates the agreement with commercial pulse oximeter. The study also evaluated the performance of the approach with different combinations of near-infrared channels.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.