Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method: a pilot evaluation study

Fan Pan; Peiyu He; Fei Chen; Yuhang Xu; Qijun Zhao; Ping Sun; Dingchang Zheng

doi:10.1097/MBP.0000000000000503

Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method: a pilot evaluation study

Fan Pan, Peiyu He, Fei Chen, Yuhang Xu, Qijun Zhao, Ping Sun, Dingchang Zheng

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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Abstract

OBJECTIVE: The aim of this study was to evaluate the performance of using a deep learning-based method for measuring SBPs and DBPs and the effects of cuff inflation and deflation rates on the deep learning-based blood pressure (BP) measurement (in comparison with the manual auscultatory method).

METHODS: Forty healthy subjects were recruited. SBP and DBP were measured under four conditions (i.e. standard deflation, fast deflation, slow inflation and fast inflation) using both our newly developed deep learning-based method and the reference manual auscultatory method. The BPs measured under each condition were compared between the two methods. The performance of using the deep learning-based method to measure BP changes was also evaluated.

RESULTS: There were no significant BP differences between the two methods (P > 0.05), except for the DBPs measured during the slow and fast inflation conditions. By applying the deep learning-based method, SBPs measured from fast deflation, slow inflation and fast inflation decreased significantly by 3.0, 3.5 and 4.7 mmHg (all P < 0.05), respectively, in comparison with the standard deflation condition. Whereas, corresponding DBPs measured from the slow and fast inflation conditions increased significantly by 5.0 and 6.8 mmHg, respectively (both P < 0.05). There were no significant differences in BP changes measured by the two methods in most cases (all P > 0.05, except for DBP change in the slow and fast inflation conditions).

CONCLUSION: This study demonstrated that the deep learning-based method can achieve accurate BP measurement under the deflation and inflation conditions with different rates.

Original language	English
Pages (from-to)	129-134
Number of pages	6
Journal	Blood Pressure Monitoring
Volume	26
Issue number	2
Early online date	23 Nov 2020
DOIs	https://doi.org/10.1097/MBP.0000000000000503
Publication status	Published - 1 Apr 2021

Bibliographical note

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.

Funder

This study was supported in part by China Postdoctoral Science Foundation (grant no. 2019M653409), in part by Chengdu Science and Technology Bureau (grant no. 2019-YF05-00109-SN), in part by Sichuan Science and Technology Program (grant no. 2020YJ0282) and in part by the National Natural Science Foundation of China (grant no. 61701050). The experiment was conducted with support from the Engineering and Physical Sciences Research Council (EPSRC) Healthcare Partnership Award (grant no. EP/I027270/1).

Keywords

blood pressure measurement
cuff deflation
cuff inflation
deep learning

ASJC Scopus subject areas

Cardiology and Cardiovascular Medicine
Assessment and Diagnosis
Advanced and Specialised Nursing
Internal Medicine

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10.1097/MBP.0000000000000503

Post-PrintAccepted author manuscript, 983 KBLicence: CC BY-NC

Cite this

Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method : a pilot evaluation study. / Pan, Fan; He, Peiyu; Chen, Fei; Xu, Yuhang; Zhao, Qijun; Sun, Ping; Zheng, Dingchang.

In: Blood Pressure Monitoring, Vol. 26, No. 2, 01.04.2021, p. 129-134.

Research output: Contribution to journal › Article › peer-review

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title = "Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method: a pilot evaluation study",

abstract = "OBJECTIVE: The aim of this study was to evaluate the performance of using a deep learning-based method for measuring SBPs and DBPs and the effects of cuff inflation and deflation rates on the deep learning-based blood pressure (BP) measurement (in comparison with the manual auscultatory method).METHODS: Forty healthy subjects were recruited. SBP and DBP were measured under four conditions (i.e. standard deflation, fast deflation, slow inflation and fast inflation) using both our newly developed deep learning-based method and the reference manual auscultatory method. The BPs measured under each condition were compared between the two methods. The performance of using the deep learning-based method to measure BP changes was also evaluated.RESULTS: There were no significant BP differences between the two methods (P > 0.05), except for the DBPs measured during the slow and fast inflation conditions. By applying the deep learning-based method, SBPs measured from fast deflation, slow inflation and fast inflation decreased significantly by 3.0, 3.5 and 4.7 mmHg (all P < 0.05), respectively, in comparison with the standard deflation condition. Whereas, corresponding DBPs measured from the slow and fast inflation conditions increased significantly by 5.0 and 6.8 mmHg, respectively (both P < 0.05). There were no significant differences in BP changes measured by the two methods in most cases (all P > 0.05, except for DBP change in the slow and fast inflation conditions).CONCLUSION: This study demonstrated that the deep learning-based method can achieve accurate BP measurement under the deflation and inflation conditions with different rates.",

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author = "Fan Pan and Peiyu He and Fei Chen and Yuhang Xu and Qijun Zhao and Ping Sun and Dingchang Zheng",

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AU - He, Peiyu

AU - Chen, Fei

AU - Xu, Yuhang

AU - Zhao, Qijun

AU - Sun, Ping

AU - Zheng, Dingchang

N1 - 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.

PY - 2021/4/1

Y1 - 2021/4/1

N2 - OBJECTIVE: The aim of this study was to evaluate the performance of using a deep learning-based method for measuring SBPs and DBPs and the effects of cuff inflation and deflation rates on the deep learning-based blood pressure (BP) measurement (in comparison with the manual auscultatory method).METHODS: Forty healthy subjects were recruited. SBP and DBP were measured under four conditions (i.e. standard deflation, fast deflation, slow inflation and fast inflation) using both our newly developed deep learning-based method and the reference manual auscultatory method. The BPs measured under each condition were compared between the two methods. The performance of using the deep learning-based method to measure BP changes was also evaluated.RESULTS: There were no significant BP differences between the two methods (P > 0.05), except for the DBPs measured during the slow and fast inflation conditions. By applying the deep learning-based method, SBPs measured from fast deflation, slow inflation and fast inflation decreased significantly by 3.0, 3.5 and 4.7 mmHg (all P < 0.05), respectively, in comparison with the standard deflation condition. Whereas, corresponding DBPs measured from the slow and fast inflation conditions increased significantly by 5.0 and 6.8 mmHg, respectively (both P < 0.05). There were no significant differences in BP changes measured by the two methods in most cases (all P > 0.05, except for DBP change in the slow and fast inflation conditions).CONCLUSION: This study demonstrated that the deep learning-based method can achieve accurate BP measurement under the deflation and inflation conditions with different rates.

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KW - blood pressure measurement

KW - cuff deflation

KW - cuff inflation

KW - deep learning

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JO - Blood Pressure Monitoring

JF - Blood Pressure Monitoring

SN - 1359-5237

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Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method: a pilot evaluation study

Abstract

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