Blood Oxygen Saturation Estimation from Facial Video via DC and AC components of Spatio-temporal Map

TL;DR

This paper introduces a CNN-based non-contact method for estimating blood oxygen saturation from facial videos by leveraging DC and AC components of RGB signals, outperforming existing techniques.

Contribution

It proposes a novel CNN approach that explicitly models DC and AC components for improved SpO2 estimation from facial videos.

Findings

Achieves higher accuracy than current state-of-the-art methods

Uses spatio-temporal maps to extract relevant features

Demonstrates effectiveness on data from 50 subjects

Abstract

Peripheral blood oxygen saturation (SpO2), an indicator of oxygen levels in the blood, is one of the most important physiological parameters. Although SpO2 is usually measured using a pulse oximeter, non-contact SpO2 estimation methods from facial or hand videos have been attracting attention in recent years. In this paper, we propose an SpO2 estimation method from facial videos based on convolutional neural networks (CNN). Our method constructs CNN models that consider the direct current (DC) and alternating current (AC) components extracted from the RGB signals of facial videos, which are important in the principle of SpO2 estimation. Specifically, we extract the DC and AC components from the spatio-temporal map using filtering processes and train CNN models to predict SpO2 from these components. We also propose an end-to-end model that predicts SpO2 directly from the spatio-temporal map by extracting the DC and AC components via convolutional layers. Experiments using facial videos and SpO2 data from 50 subjects demonstrate that the proposed method achieves a better estimation performance than current state-of-the-art SpO2 estimation methods.