Deep learning based on Convolutional Neural Network (CNN) has shown promising
results in various vision-based applications, recently also in camera-based
vital signs monitoring. The CNN-based Photoplethysmography (PPG) extraction
has, so far, been focused on performance rather than understanding. In this
paper, we try to answer four questions with experiments aiming at improving our
understanding of this methodology as it gains popularity. We conclude that the
network exploits the blood absorption variation to extract the physiological
signals, and that the choice and parameters (phase, spectral content, etc.) of
the reference-signal may be more critical than anticipated. The availability of
multiple convolutional kernels is necessary for CNN to arrive at a flexible
channel combination through the spatial operation, but may not provide the same
motion-robustness as a multi-site measurement using knowledge-based PPG
extraction. Finally, we conclude that the PPG-related prior knowledge is still
helpful for the CNN-based PPG extraction. Consequently, we recommend further
investigation of hybrid CNN-based methods to include prior knowledge in their
design.

本文探讨了基于卷积神经网络（CNN）的心率监测中，参考信号的选择以及卷积核的多样性等因素对性能的影响，结果说明 CNN 基于血吸收变化提取了生理信号，同时证明了 PPG 相关先验知识有助于提高其性能，因此，建议今后将先验知识纳入混合 CNN 方法中进行研究。