Zhang Yawen, Tsujikawa Masanori, Onishi Yoshifumi
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:3226-3230. doi: 10.1109/EMBC.2019.8857097.
This paper proposes a remote sleep/wake classification method by combining vision-based heart rate (HR) estimation and convolutional neural network (CNN). Instead of inputting the estimated HR with low temporal resolution, remote PPG (Photoplethysmogram) signals, which contain high-temporal-resolution HR information, are input into the CNN. To reduce noise in the remote PPG signals, we propose a dynamic HR filter. Evaluation results show that the dynamic HR filter works more effectively in comparison with the static filter, which helps improve the area under the ROC curve (AUC) to 0.70, which is almost as good as the reference 0.71 for HR from a wearable sensor.
本文提出了一种通过结合基于视觉的心率（HR）估计和卷积神经网络（CNN）进行远程睡眠/唤醒分类的方法。不是将时间分辨率低的估计心率输入，而是将包含高时间分辨率心率信息的远程光电容积脉搏波（PPG）信号输入到CNN中。为了降低远程PPG信号中的噪声，我们提出了一种动态HR滤波器。评估结果表明，与静态滤波器相比，动态HR滤波器的效果更佳，这有助于将ROC曲线下面积（AUC）提高到0.70，几乎与可穿戴传感器心率参考值0.71相当。
