Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China.
Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Ashby Road, Loughborough, Leicestershire LE11 3TU, UK.
J Healthc Eng. 2020 Feb 10;2020:1078251. doi: 10.1155/2020/1078251. eCollection 2020.
Continuous blood pressure (BP) monitoring has a significant meaning for the prevention and early diagnosis of cardiovascular disease. However, under different calibration methods, it is difficult to determine which model is better for estimating BP. This study was firstly designed to reveal a better BP estimation model by evaluating and optimizing different BP models under a justified and uniform criterion, i.e., the advanced point-to-point pairing method (PTP). Here, the physical trial in this study caused the BP increase largely. In addition, the PPG and ECG signals were collected while the cuff bps were measured for each subject. The validation was conducted on four popular vascular elasticity (VE) models (MK-EE, L-MK, MK-BH, and dMK-BH) and one representative elastic tube (ET) model, i.e., M-M. The results revealed that the VE models except for L-MK outperformed the ET model. The linear L-MK as a VE model had the largest estimated error, and the nonlinear M-M model had a weaker correlation between the estimated BP and the cuff BP than MK-EE, MK-BH, and dMK-BH models. Further, in contrast to L-MK, the dMK-BH model had the strongest correlation and the smallest difference between the estimated BP and the cuff BP including systolic blood pressure (SBP) and diastolic blood pressure (DBP) than others. In this study, the simple MK-EE model showed the best similarity to the dMK-BH model. There were no significant changes between MK-EE and dMK-BH models. These findings indicated that the nonlinear MK-EE model with low estimated error and simple mathematical expression was a good choice for application in wearable sensor devices for cuff-less BP monitoring compared to others.
连续血压（BP）监测对心血管疾病的预防和早期诊断具有重要意义。然而，在不同的校准方法下，很难确定哪种模型更适合估计 BP。本研究旨在通过评估和优化不同的 BP 模型，使用合理和统一的标准，即先进的点对点配对方法（PTP），来揭示更好的 BP 估计模型。在这里，研究中的物理试验导致血压大幅升高。此外，在为每个受试者测量袖带血压的同时，还采集了 PPG 和 ECG 信号。验证是在四个流行的血管弹性（VE）模型（MK-EE、L-MK、MK-BH 和 dMK-BH）和一个代表性弹性管（ET）模型，即 M-M 上进行的。结果表明，除 L-MK 外，VE 模型均优于 ET 模型。作为 VE 模型的线性 L-MK 具有最大的估计误差，而非线性 M-M 模型的估计 BP 与袖带 BP 之间的相关性弱于 MK-EE、MK-BH 和 dMK-BH 模型。此外，与 L-MK 相比，dMK-BH 模型在估计 BP 和袖带 BP 之间具有最强的相关性和最小的差异，包括收缩压（SBP）和舒张压（DBP）。在这项研究中，简单的 MK-EE 模型与 dMK-BH 模型显示出最好的相似性。MK-EE 和 dMK-BH 模型之间没有显著差异。这些发现表明，与其他模型相比，非线性 MK-EE 模型具有较低的估计误差和简单的数学表达式，是可穿戴传感器设备无袖带 BP 监测应用的不错选择。
