论文标题
通过直流和时空图的AC成分从面部视频中估算血氧饱和度
Blood Oxygen Saturation Estimation from Facial Video via DC and AC components of Spatio-temporal Map
论文作者
论文摘要
外周血氧饱和度（SPO2）是血液中氧气水平的指标，是最重要的生理参数之一。尽管通常使用脉搏血氧仪测量SPO2，但近年来，来自面部或手动视频的非接触式SPO2估计方法吸引了人们的注意。在本文中，我们提出了一种基于卷积神经网络（CNN）的面部视频的SPO2估计方法。我们的方法构建了CNN模型，这些模型考虑了从面部视频的RGB信号中提取的直流电流（DC）和交替的电流（AC）组件，这在SPO2估计的原理中很重要。具体而言，我们使用过滤过程和训练CNN模型从时空图中提取DC和AC成分，以预测这些组件中的SPO2。我们还提出了一个端到端模型，该模型通过通过卷积层提取DC和AC组件直接从时空图中预测SPO2。使用来自50名受试者的面部视频和SPO2数据的实验表明，所提出的方法比当前最新的SPO2估计方法获得了更好的估计性能。
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.
