Key result
Hemodynamics-based P-β model improves cuffless blood pressure estimation accuracy, meeting ISO validation standards.
Why the study?
Non-invasive devices that continuously monitor BP provide more comprehensive BP assessment to inform therapeutic decisions, motivating the development of a small cuffless monitoring device.
Does a cuffless BP monitoring device using a P-β model improve blood pressure estimation accuracy compared to standard regression models in adult participants?
Does a cuffless BP monitoring device using a P-β model improve blood pressure estimation accuracy compared to standard regression models in adult participants?
A novel multi-sensor cuffless BP monitor utilizing a hemodynamic P-β model meets international accuracy standards and offers reduced measurement dispersion compared to conventional regression techniques.
Answers are grounded in this paper and cite their sources.
Advances cuffless BP monitoring feasibility; leaves open larger validation trials before clinical adoption.
OBJECTIVE: Blood pressure (BP) data can influence therapeutic decisions for some patients, while non-invasive devices that continuously monitor BP can provide patients with a more comprehensive BP assessment. Therefore, this study proposes a multi-sensor-based small cuffless BP monitoring device that integrates a piezoelectric sensor array and an optical sensor, which can monitor the patient's physiological signals from the radial artery. METHOD: Based on the Moens-Korteweg (MK) equation of the hemodynamic model, pulse wave velocity (PWV) can be correlated with arterial compliance and BP can be estimated. Therefore, the novel method proposed in this study involves using a piezoelectric sensor array to measure the PWV and an optical sensor to measure the photoplethysmography (PPG) intensity ratio (PIR) signal to estimate the participant's arterial parameters. The parameters measured by multiple sensors were combined to estimate BP based on the P-β model derived from the MK equation. RESULT: We recruited 20 participants for the BP monitoring experiment to compare the performance of the BP estimation method with the regression model and the P-β model method with arterial compliance. We then compared the estimated BP with a reference device for validation. The results are presented as the error mean ± standard deviation (SD). Based on the regression model method, systolic blood pressure (SBP) was 0.32 ± 5.94, diastolic blood pressure (DBP) was 2.17 ± 6.22, and mean arterial pressure (MAP) was 1.55 ± 5.83. The results of the P-β model method were as follows: SBP was 0.75 ± 3.9, DBP was 1.1 ± 3.12, and MAP was 0.49 ± 2.82. CONCLUSION: According to the results of our proposed small cuffless BP monitoring device, both methods of estimating BP conform to ANSI/AAMI/ISO 81060-2:20181_5.2.4.1.2 criterion 1 and 2, and using arterial parameters to calibrate the MK equation model can improve BP estimate accuracy. In the future, our proposed device can provide patients with a convenient and comfortable BP monitoring solution. Since the device is small, it can be used in a public place without attracting other people's attention, thereby effectively improving the patient's right to privacy, and increasing their willingness to use it.
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Guo et al. (2022) studied this question. P-β model BP estimation (multi-sensor cuffless device) vs. Regression model BP estimation was evaluated on Mean error ± standard deviation of Systolic Blood Pressure (SBP) estimation. The P-β model method improved blood pressure estimation accuracy with less dispersion than the regression model, achieving an SBP error of 0.75 ± 3.9 and conforming to ISO 81060-2:2018 standards.
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