Development and validation of a deep learning-based automatic auscultatory blood pressure measurement method

Fan Pan; Peiyu He; He Wang; Yuhang Xu; Xiaobo Pu; Qijun Zhao; Fei Chen; Dingchang Zheng

doi:10.1016/j.bspc.2021.102742

Development and validation of a deep learning-based automatic auscultatory blood pressure measurement method

Fan Pan, Peiyu He, He Wang, Yuhang Xu, Xiaobo Pu, Qijun Zhao, Fei Chen, Dingchang Zheng

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Abstract

Manual auscultatory is the gold standard for clinical non-invasive blood pressure (BP) measurement, but its usage is decreasing as it requires substantial professional skills and training, and its environmental concerns related to mercury toxicity. As an alternative, automatic oscillometric technique has been used as one of the most common methods for BP measurement, however, it only estimates BPs based on empirical equations. To overcome these problems, this study aimed to develop a deep learning-based automatic auscultatory BP measurement method, and clinically validate its performance. A deep learning-based method that utilized time-frequency characteristics and temporal dependence of segmented Korotkoff sound (KorS) signals and employed convolutional neural network (CNN) and long short-term memory (LSTM) network was developed and trained using KorS and cuff pressure signals recorded from 314 subjects. The BPs determined by the manual auscultatory method was used as the reference for each measurement. The measurement error and BP category classification performance of our proposed method were then validated on a separate dataset of 114 subjects. Its performance in comparison with the oscillometric method was also comprehensively analyzed. The deep learning method achieved measurement errors of 0.2 ± 4.6 mmHg and 0.1 ± 3.2 mmHg for systolic BP and diastolic BP, respectively, and achieved high sensitivity, specificity and accuracy (all > 90 %) in classifying hypertensive subjects, which were better than those of the traditional oscillometric method. This validation study demonstrated that deep learning-based automatic auscultatory BP measurement can be developed to achieve high measurement accuracy and high BP category classification performance.

Original language	English
Article number	102742
Journal	Biomedical Signal Processing and Control
Volume	68
Early online date	10 May 2021
DOIs	https://doi.org/10.1016/j.bspc.2021.102742
Publication status	Published - Jul 2021

Bibliographical note

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Funder

China Postdoctoral Science Foundation (Grant number 2019M653409 ), Chengdu Science and Technology Bureau (Grant number 2019-YF05-00109-SN ), Sichuan Science and Technology Program (Grant number 2020YJ0282 ), and High-level University Fund of Southern University of Science and Technology . The experiment was conducted with the support from the Engineering and Physical Sciences Research Council (EPSRC) Healthcare Partnership Award (Grant number EP/I027270/1 ).

Keywords

Blood pressure measurement
Deep learning
Manual auscultatory method
Oscillometric method

ASJC Scopus subject areas

Signal Processing
Health Informatics

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10.1016/j.bspc.2021.102742

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Cite this

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abstract = "Manual auscultatory is the gold standard for clinical non-invasive blood pressure (BP) measurement, but its usage is decreasing as it requires substantial professional skills and training, and its environmental concerns related to mercury toxicity. As an alternative, automatic oscillometric technique has been used as one of the most common methods for BP measurement, however, it only estimates BPs based on empirical equations. To overcome these problems, this study aimed to develop a deep learning-based automatic auscultatory BP measurement method, and clinically validate its performance. A deep learning-based method that utilized time-frequency characteristics and temporal dependence of segmented Korotkoff sound (KorS) signals and employed convolutional neural network (CNN) and long short-term memory (LSTM) network was developed and trained using KorS and cuff pressure signals recorded from 314 subjects. The BPs determined by the manual auscultatory method was used as the reference for each measurement. The measurement error and BP category classification performance of our proposed method were then validated on a separate dataset of 114 subjects. Its performance in comparison with the oscillometric method was also comprehensively analyzed. The deep learning method achieved measurement errors of 0.2 ± 4.6 mmHg and 0.1 ± 3.2 mmHg for systolic BP and diastolic BP, respectively, and achieved high sensitivity, specificity and accuracy (all > 90 %) in classifying hypertensive subjects, which were better than those of the traditional oscillometric method. This validation study demonstrated that deep learning-based automatic auscultatory BP measurement can be developed to achieve high measurement accuracy and high BP category classification performance.",

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AU - Zheng, Dingchang

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Development and validation of a deep learning-based automatic auscultatory blood pressure measurement method

Abstract

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