  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.
