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

Pan, Fan1; He, Peiyu1; Chen, Fei2; Pu, Xiaobo3; Zhao, Qijun4; Zheng, Dingchang5

2019-11-17

10.1080/07853890.2019.1694170

ANNALS OF MEDICINE   影响因子和分区

0785-3890

1365-2060

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.

Blood pressure measurement deep learning manual auscultatory method measurement condition

SCI

英语

其他

General & Internal Medicine

Medicine, General & Internal

WOS:000499041000007

TAYLOR & FRANCIS LTD

CLINICAL MEDICINE

Web of Science

1.Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610064, Sichuan, Peoples R China
2.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Guangdong, Peoples R China
3.Sichuan Univ, West China Hosp, Dept Cardiol, Chengdu, Sichuan, Peoples R China
4.Sichuan Univ, Coll Comp Sci, Chengdu, Sichuan, Peoples R China
5.Coventry Univ, Fac Hlth & Life Sci, Res Ctr Intelligent Healthcare, Coventry CV1 5FB, W Midlands, England

Pan, Fan,He, Peiyu,Chen, Fei,et al. Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement[J]. ANNALS OF MEDICINE,2019,51(7-8):397-403.

Pan, Fan,He, Peiyu,Chen, Fei,Pu, Xiaobo,Zhao, Qijun,&Zheng, Dingchang.(2019).Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement.ANNALS OF MEDICINE,51(7-8),397-403.

Pan, Fan,et al."Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement".ANNALS OF MEDICINE 51.7-8(2019):397-403.