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A device employing a neural network for blood pressure estimation from the oscillatory pressure pulse wave and PPG signal

Jian Tian (Chongqing University of Posts and Telecommunications, Chongqing, China)
Jiangan Xie (Chongqing University of Posts and Telecommunications, Chongqing, China)
Zhonghua He (Chongqing University of Posts and Telecommunications, Chongqing, China)
Qianfeng Ma (Chongqing University of Posts and Telecommunications, Chongqing, China)
Xiuxin Wang (Chongqing University of Posts and Telecommunications, Chongqing, China and Key Laboratory of Opto-Technology and Intelligent Control, Ministry of Education, Lanzhou Jiao tong University, Lanzhou, China)

Sensor Review

ISSN: 0260-2288

Article publication date: 3 February 2021

Issue publication date: 24 February 2021

204

Abstract

Purpose

Wrist-cuff oscillometric blood pressure monitors are very popular in the portable medical device market. However, its accuracy has always been controversial. In addition to the oscillatory pressure pulse wave, the finger photoplethysmography (PPG) can provide information on blood pressure changes. A blood pressure measurement system integrating the information of pressure pulse wave and the finger PPG may improve measurement accuracy. Additionally, a neural network can synthesize the information of different types of signals and approximate the complex nonlinear relationship between inputs and outputs. The purpose of this study is to verify the hypothesis that a wrist-cuff device using a neural network for blood pressure estimation from both the oscillatory pressure pulse wave and PPG signal may improve the accuracy.

Design/methodology/approach

A PPG sensor was integrated into a wrist blood pressure monitor, so the finger PPG and the oscillatory pressure wave could be detected at the same time during the measurement. After the peak detection, curves were fitted to the data of pressure pulse amplitude and PPG pulse amplitude versus time. A genetic algorithm-back propagation neural network was constructed. Parameters of the curves were inputted into the neural network, the outputs of which were the measurement values of blood pressure. Blood pressure measurements of 145 subjects were obtained using a mercury sphygmomanometer, the developed device with the neural network algorithm and an Omron HEM-6111 blood pressure monitor for comparison.

Findings

For the systolic blood pressure (SBP), the difference between the proposed device and the mercury sphygmomanometer is 0.0062 ± 2.55 mmHg (mean ± SD) and the difference between the Omron device and the mercury sphygmomanometer is 1.13 ± 9.48 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and the proposed device was 0.28 ± 2.99 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and Omron HEM-6111 was −3.37 ± 7.53 mmHg.

Originality/value

Although the difference in the SBP error between the proposed device and Omron HEM-6111 was not remarkable, there was a significant difference between the proposed device and Omron HEM-6111 in the diastolic blood pressure error. The developed device showed an improved performance. This study was an attempt to enhance the accuracy of wrist-cuff oscillometric blood pressure monitors by using the finger PPG and the neural network. The hardware framework constructed in this study can improve the conventional wrist oscillometric sphygmomanometer and may be used for continuous measurement of blood pressure.

Keywords

Acknowledgements

Opening Foundations of Key Laboratory of Opto-technology and Intelligent Control (Lanzhou Jiao tong University), Ministry of Education.KFKT2020-08.National Key Research and Development Program of China.2019YFC2003301.National Natural Science Foundation of China.61801067.Natural Science Foundation of Chongqing.cstc2018jcyjAX0243.PhD Start-up Fund of Chongqing University of Posts and Telecommunications.A2017‐133.Science and Technology Research Program of Chongqing Municipal Education Commission.KJQN202000636.

Citation

Tian, J., Xie, J., He, Z., Ma, Q. and Wang, X. (2021), "A device employing a neural network for blood pressure estimation from the oscillatory pressure pulse wave and PPG signal", Sensor Review, Vol. 41 No. 1, pp. 74-86. https://doi.org/10.1108/SR-09-2020-0216

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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