Oxygen saturation estimation from near-infrared multispectral video data using 3D convolutional residual networks

Wang Liao; Chen Zhang; Xinyu Sun; Gunther Notni

doi:10.1117/12.2673109

9 August 2023 Oxygen saturation estimation from near-infrared multispectral video data using 3D convolutional residual networks

Wang Liao, Chen Zhang, Xinyu Sun, Gunther Notni

Author Affiliations +

Proceedings Volume 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III; 126210O (2023) https://doi.org/10.1117/12.2673109
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

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.

Conference Presentation

Citation Download Citation

Wang Liao, Chen Zhang, Xinyu Sun, and Gunther Notni "Oxygen saturation estimation from near-infrared multispectral video data using 3D convolutional residual networks", Proc. SPIE 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, 126210O (9 August 2023); https://doi.org/10.1117/12.2673109

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