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.

翻译：虽然SpO2通常使用脉冲氧计测量,但脸部或手部视频的非接触SpO2估计方法近年来一直引起注意。在本文中,我们提议了基于神经神经网络(CNN)的面部视频的SpO2估计方法。我们的方法构建了CNN模型,这些模型考虑直接流(DC)和从血表视频RGB信号中提取的交替流(AC)组件,这在SpO2估计原则中很重要。具体地说,我们利用过滤程序从spatio-时间图中提取DC和AC组件,并培训CNN模型从这些组件中预测SpO2。我们还提议了一个端对端模型,通过光谱层提取DC和AC组件,直接从spatio-时间图中预测SpO2。使用面部视频和SpO2数据进行实验表明,拟议方法的性能比目前状态-状态-O2估算方法要好。