Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement

Fan Pan, Peiyu He, Fei Chen, Xiaobo Pu, Qijun Zhao, D. Zheng

Research output: Contribution to journalArticle

Abstract

Objectives: It is clinically important to evaluate the performance of a newly developed blood pressure (BP) measurement method under different measurement conditions. This study aims to evaluate the performance of using deep learning-based method to measure BPs and BP change under non-resting conditions. Materials and methods: Forty healthy subjects were studied. Systolic and diastolic BPs (SBPs and DBPs) were measured under four conditions using deep learning and manual auscultatory method. The agreement between BPs determined by the two methods were analysed under different conditions. The performance of using deep learning-based method to measure BP changes was finally evaluated. Results: There were no significant BPs differences between two methods under all measurement conditions (all p > .1). SBP and DBP measured by deep learning method changed significantly in comparison with the resting condition: decreased by 2.3 and 4.2 mmHg with deeper breathing (both p < .05), increased by 3.6 and 6.4 mmHg with talking, and increased by 5.9 and 5.8 mmHg with arm movement (all p < .05). There were no significant differences in BP changes measured by two methods (all p > .4, except for SBP change with deeper breathing). Conclusion: This study demonstrated that the deep learning method could achieve accurate BP measurement under both resting and non-resting conditions. Key messages Accurate and reliable blood pressure measurement is clinically important. We evaluated the performance of our developed deep learning-based blood pressure measurement method under resting and non-resting measurement conditions. The deep learning-based method could achieve accurate BP measurement under both resting and non-resting measurement conditions.
Original languageEnglish
Pages (from-to)397-403
Number of pages7
JournalAnnals of Medicine
Volume51
Issue number7-8
Early online date28 Nov 2019
DOIs
Publication statusE-pub ahead of print - 28 Nov 2019

Fingerprint

Blood pressure
Pressure measurement
Deep learning

Bibliographical note

This is an Accepted Manuscript of an article published by Taylor & Francis in Annals of Medicine on 28/11/2019 available online: http://www.tandfonline.com/10.1080/07853890.2019.1694170

Keywords

  • Blood pressure measurement
  • Deep learning
  • Manual auscultatory method
  • Measurement condition

Cite this

Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement. / Pan, Fan; He, Peiyu; Chen, Fei; Pu, Xiaobo; Zhao, Qijun; Zheng, D.

In: Annals of Medicine, Vol. 51, No. 7-8, 28.11.2019, p. 397-403.

Research output: Contribution to journalArticle

Pan, Fan ; He, Peiyu ; Chen, Fei ; Pu, Xiaobo ; Zhao, Qijun ; Zheng, D. / Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement. In: Annals of Medicine. 2019 ; Vol. 51, No. 7-8. pp. 397-403.
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abstract = "Objectives: It is clinically important to evaluate the performance of a newly developed blood pressure (BP) measurement method under different measurement conditions. This study aims to evaluate the performance of using deep learning-based method to measure BPs and BP change under non-resting conditions. Materials and methods: Forty healthy subjects were studied. Systolic and diastolic BPs (SBPs and DBPs) were measured under four conditions using deep learning and manual auscultatory method. The agreement between BPs determined by the two methods were analysed under different conditions. The performance of using deep learning-based method to measure BP changes was finally evaluated. Results: There were no significant BPs differences between two methods under all measurement conditions (all p > .1). SBP and DBP measured by deep learning method changed significantly in comparison with the resting condition: decreased by 2.3 and 4.2 mmHg with deeper breathing (both p < .05), increased by 3.6 and 6.4 mmHg with talking, and increased by 5.9 and 5.8 mmHg with arm movement (all p < .05). There were no significant differences in BP changes measured by two methods (all p > .4, except for SBP change with deeper breathing). Conclusion: This study demonstrated that the deep learning method could achieve accurate BP measurement under both resting and non-resting conditions. Key messages Accurate and reliable blood pressure measurement is clinically important. We evaluated the performance of our developed deep learning-based blood pressure measurement method under resting and non-resting measurement conditions. The deep learning-based method could achieve accurate BP measurement under both resting and non-resting measurement conditions.",
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AU - Zheng, D.

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N2 - Objectives: It is clinically important to evaluate the performance of a newly developed blood pressure (BP) measurement method under different measurement conditions. This study aims to evaluate the performance of using deep learning-based method to measure BPs and BP change under non-resting conditions. Materials and methods: Forty healthy subjects were studied. Systolic and diastolic BPs (SBPs and DBPs) were measured under four conditions using deep learning and manual auscultatory method. The agreement between BPs determined by the two methods were analysed under different conditions. The performance of using deep learning-based method to measure BP changes was finally evaluated. Results: There were no significant BPs differences between two methods under all measurement conditions (all p > .1). SBP and DBP measured by deep learning method changed significantly in comparison with the resting condition: decreased by 2.3 and 4.2 mmHg with deeper breathing (both p < .05), increased by 3.6 and 6.4 mmHg with talking, and increased by 5.9 and 5.8 mmHg with arm movement (all p < .05). There were no significant differences in BP changes measured by two methods (all p > .4, except for SBP change with deeper breathing). Conclusion: This study demonstrated that the deep learning method could achieve accurate BP measurement under both resting and non-resting conditions. Key messages Accurate and reliable blood pressure measurement is clinically important. We evaluated the performance of our developed deep learning-based blood pressure measurement method under resting and non-resting measurement conditions. The deep learning-based method could achieve accurate BP measurement under both resting and non-resting measurement conditions.

AB - Objectives: It is clinically important to evaluate the performance of a newly developed blood pressure (BP) measurement method under different measurement conditions. This study aims to evaluate the performance of using deep learning-based method to measure BPs and BP change under non-resting conditions. Materials and methods: Forty healthy subjects were studied. Systolic and diastolic BPs (SBPs and DBPs) were measured under four conditions using deep learning and manual auscultatory method. The agreement between BPs determined by the two methods were analysed under different conditions. The performance of using deep learning-based method to measure BP changes was finally evaluated. Results: There were no significant BPs differences between two methods under all measurement conditions (all p > .1). SBP and DBP measured by deep learning method changed significantly in comparison with the resting condition: decreased by 2.3 and 4.2 mmHg with deeper breathing (both p < .05), increased by 3.6 and 6.4 mmHg with talking, and increased by 5.9 and 5.8 mmHg with arm movement (all p < .05). There were no significant differences in BP changes measured by two methods (all p > .4, except for SBP change with deeper breathing). Conclusion: This study demonstrated that the deep learning method could achieve accurate BP measurement under both resting and non-resting conditions. Key messages Accurate and reliable blood pressure measurement is clinically important. We evaluated the performance of our developed deep learning-based blood pressure measurement method under resting and non-resting measurement conditions. The deep learning-based method could achieve accurate BP measurement under both resting and non-resting measurement conditions.

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KW - Deep learning

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