Remote Estimation of Continuous Blood Pressure by a Convolutional Neural Network Trained on Spatial Patterns of Facial Pulse Waves

We propose a remote method to estimate continuous blood pressure based on spatial information of a pulse wave at a single point in 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 blood pressure is modeled by a convolutional neural network. A dataset was built to demonstrate the effectiveness of the proposed method. The dataset consists of continuous blood pressure and facial RGB videos of ten healthy volunteers. A comparison of conventional methods with the proposed method shows superior error for the latter. The results show an adequate estimation of the performance of the proposed method, when compared to the ground truth in mean blood pressure, in both the correlation coefficient (0.85) and mean absolute error (5.4 mmHg).