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

Pan，Fan1; He，Peiyu1; Chen，Fei2; Xu，Yuhang3; Zhao，Qijun4; Sun，Ping5; Zheng，Dingchang3

2021-04-01

10.1097/MBP.0000000000000503

BLOOD PRESSURE MONITORING   影响因子和分区

1359-5237

1473-5725

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.

blood pressure measurement cuff deflation cuff inflation deep learning

SCI

英语

其他

China Postdoctoral Science Foundation[2019M653409] ; Chengdu Science and Technology Bureau[2019-YF05-00109-SN] ; Sichuan Science and Technology Program[2020YJ0282] ; National Natural Science Foundation of China[61701050] ; Engineering and Physical Sciences Research Council (EPSRC) Healthcare Partnership Award[EP/I027270/1]

Cardiovascular System & Cardiology

Peripheral Vascular Disease

WOS:000639295000008

LIPPINCOTT WILLIAMS & WILKINS

CLINICAL MEDICINE

2-s2.0-85102602495

Scopus

1.College of Electronics and Information Engineering,Sichuan University,Chengdu,China
2.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,China
3.Research Centre of Intelligent Healthcare,Faculty of Health and Life Science,Coventry University,Coventry,United Kingdom
4.College of Computer Science,Sichuan University,
5.College of Optoelectronic Engineering,Chengdu University of Information Technology,Chengdu,China

Pan，Fan,He，Peiyu,Chen，Fei,et al. Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method: a pilot evaluation study[J]. BLOOD PRESSURE MONITORING,2021,26(2):129-134.

Pan，Fan.,He，Peiyu.,Chen，Fei.,Xu，Yuhang.,Zhao，Qijun.,...&Zheng，Dingchang.(2021).Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method: a pilot evaluation study.BLOOD PRESSURE MONITORING,26(2),129-134.

Pan，Fan,et al."Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method: a pilot evaluation study".BLOOD PRESSURE MONITORING 26.2(2021):129-134.