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 journalArticlepeer-review

    2 Citations (Scopus)
    94 Downloads (Pure)


    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 languageEnglish
    Pages (from-to)129-134
    Number of pages6
    JournalBlood Pressure Monitoring
    Issue number2
    Early online date23 Nov 2020
    Publication statusPublished - 1 Apr 2021

    Bibliographical note

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    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).


    • 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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