A non-contact continuous blood pressure measurement method based on video stream.
10.7507/1001-5515.202211071
- Author:
Hao YAN
1
;
Xia LI
1
;
Tianyang ZHU
1
;
Xiuqiang CHEN
1
;
Ning GONG
1
;
Qinwu ZHOU
1
Author Information
1. School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China.
- Publication Type:Journal Article
- Keywords:
Continuous blood pressure measurement;
Feature selection;
Independent component analysis;
Machine learning;
Support vector machine
- MeSH:
Humans;
Blood Pressure/physiology*;
Blood Pressure Determination/methods*;
Algorithms;
Hypertension/diagnosis*;
Sexually Transmitted Diseases
- From:
Journal of Biomedical Engineering
2023;40(2):249-256
- CountryChina
- Language:Chinese
-
Abstract:
Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.
