An Optimization Study of Estimating Blood Pressure Models.pdf (1.35 MB)
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An optimization study of estimating blood pressure models based on pulse arrival time for continuous monitoring

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journal contribution
posted on 14.02.2020, 11:51 authored by Jiang Shao, Ping Shi, Sijung HuSijung Hu, Yang Liu, Hongliu Yu
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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Journal of Healthcare Engineering

Volume

2020

Publisher

Hindawi Limited

Version

VoR (Version of Record)

Rights holder

© the Authors

Publisher statement

This is an Open Access Article. It is published by Hindawi Limited under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

19/10/2019

Publication date

2020-02-10

Copyright date

2020

ISSN

2040-2295

eISSN

2040-2309

Language

en

Depositor

Dr Sijung Hu . Deposit date: 12 February 2020

Article number

1078251

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