Open AccessBlood pressure estimation by spatial pulse-wave dynamics in a facial video
Author(s) -
Kaito Iuchi,
Ryogo Miyazaki,
George C. Cardoso,
Keiko Ogawa-Ochia,
Norimichi Tsumura
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.473166
Subject(s) - computer science , artificial intelligence , ground truth , rgb color model , pulse (music) , beat (acoustics) , pattern recognition (psychology) , computer vision , spatial analysis , signal (programming language) , convolutional neural network , mathematics , acoustics , physics , telecommunications , statistics , detector , programming language
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.