Iuchi Kaito, Miyazaki Ryogo, Cardoso George C, Ogawa-Ochiai Keiko, Tsumura Norimichi
Graduate School of Science and Engineering, Department of Imaging Science, Chiba University, Japan.
Equal contribution.
Biomed Opt Express. 2022 Oct 25;13(11):6035-6047. doi: 10.1364/BOE.473166. eCollection 2022 Nov 1.
We propose a remote method to estimate continuous blood pressure (BP) based on spatial information of a pulse-wave as a function of time. By setting regions of interest to cover a face in a mutually exclusive and collectively exhaustive manner, RGB facial video is converted into a spatial pulse-wave signal. The spatial pulse-wave signal is converted into spatial signals of contours of each segmented pulse beat and relationships of each segmented pulse beat. The spatial signal is represented as a time-continuous value based on a representation of a pulse contour in a time axis and a phase axis and an interpolation along with the time axis. A relationship between the spatial signals and BP is modeled by a convolutional neural network. A dataset was built to demonstrate the effectiveness of the proposed method. The dataset consists of continuous BP and facial RGB videos of ten healthy volunteers. The results show an adequate estimation of the performance of the proposed method when compared to the ground truth in mean BP, in both the correlation coefficient (0.85) and mean absolute error (5.4 mmHg). For comparison, the dataset was processed using conventional pulse features, and the estimation error produced by our method was significantly lower. To visualize the root source of the BP signals used by our method, we have visualized spatial-wise and channel-wise contributions to the estimation by the deep learning model. The result suggests the spatial-wise contribution pattern depends on the blood pressure, while the pattern of pulse contour-wise contribution pattern reflects the relationship between percussion wave and dicrotic wave.
我们提出了一种基于随时间变化的脉搏波空间信息来估计连续血压（BP）的远程方法。通过以互斥且完备的方式设置感兴趣区域以覆盖面部，将RGB面部视频转换为空间脉搏波信号。该空间脉搏波信号被转换为每个分割脉搏搏动的轮廓的空间信号以及每个分割脉搏搏动之间的关系。基于在时间轴和相位轴上的脉搏轮廓表示以及沿时间轴的插值，将该空间信号表示为时间连续值。通过卷积神经网络对空间信号与血压之间的关系进行建模。构建了一个数据集来证明所提出方法的有效性。该数据集由十名健康志愿者的连续血压和面部RGB视频组成。结果表明，与真实血压相比，所提出方法在平均血压方面的性能估计足够，相关系数为0.85，平均绝对误差为5.4 mmHg。作为比较，使用传统脉搏特征对该数据集进行处理，我们的方法产生的估计误差明显更低。为了可视化我们方法所使用的血压信号的根源，我们通过深度学习模型可视化了在空间和通道方面对估计的贡献。结果表明，空间方面的贡献模式取决于血压，而脉搏轮廓方面的贡献模式反映了叩击波与重搏波之间的关系。
