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

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 MESSAGESAccurate 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.

Combating Hypertension Beyond GWAS: Microbiome and Artificial Intelligence as Opportunities for Precision Medicine

“…At the 6-month follow-up, the researchers did not discover decreased BP, but they did create a space for the possibility of different treatment effects among age subgroups (Persell et al, 2020 ). Pan et al ( 2019 ) coupled auscultatory waveforms data with ML to measure BP from Korotkoff sound recordings and examine the impact of movement disturbance on BP regulation. Among 40 healthy volunteers, their brand-new DL-based automatic BP measurement instrument showed encouraging accuracy in BP monitoring both when resting and not resting (Pan et al, 2019 ).…”

Development and validation of a deep learning-based automatic auscultatory blood pressure measurement method

“…We have recently developed a new deep learning-based automatic auscultatory BP measurement method in a preliminary study and evaluated its performance under different measurement conditions (resting, deeper breathing, talking and arm movement) with normotensive subjects. 17,18 These investigations have demonstrated the significant potential of using deep learning technique to automatically measure BP accurately. However, its performance has not been clinically validated on subjects with a wide range of BPs, and also has not been compared to the commonly used automatic oscillometric BP measurement method.…”

Variation of peripheral pulse transit time with internal vascular pressure changes induced by arm movement

“…Previous studies demonstrated that the value of BP measurement was influenced by the arm position. Netea et al (2003), Fouladi et al (2018), and Pan et al (2019) showed that the BP values recorded with the left arm above and below the level of the right atrium decreased with the lifting of the arm but increased with the lowering of the arm, which was explained to be the effect of hydrostatic forces (Merendino, 1961;Webster et al, 1984). However, recent studies showed that the hydrostatic theory is not the only explanation for the change in BP along the arm.…”