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Estimation of Central Aortic Blood Pressure From Non-Invasive Cuff Pressure Oscillation Signals via System Identification

[+] Author Affiliations
Zahra Ghasemi, Chang-Sei Kim, Jin-Oh Hahn

University of Maryland, College Park, MD

Eric Ginsberg, John Duell, Anuj Gupta

University of Maryland, Baltimore, MD

Paper No. DSCC2016-9785, pp. V001T10A001; 7 pages
doi:10.1115/DSCC2016-9785
From:
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME

abstract

This paper presents a model-based system identification approach to estimation of central aortic blood pressure waveform from non-invasive cuff pressure oscillation signals. First, we developed a mathematical model that can reproduce the relationship between central aortic blood pressure waveform and non-invasive cuff pressure oscillation signals at diametric locations by combining models to represent wave propagation in the artery, arterial pressure-volume relationship, and mechanics of the occlusive cuff. Second, we formulated the problem of estimating central aortic blood pressure waveform from non-invasive cuff pressure oscillation signals into a system identification problem. Third, we showed the proof-of-concept of the approach using simulated central aortic blood pressure waveform and cuff pressure oscillation signals. Finally, we illustrated the feasibility of the approach using central aortic blood pressure waveform and cuff pressure oscillation signals collected from a human subject. We showed that the proposed approach could estimate central aortic blood pressure waveform with accuracy: the root-mean-squared error associated with the central aortic blood pressure waveform was 1.7 mmHg (amounting to 1.6 % of the underlying mean blood pressure) while the errors associated with central aortic systolic and pulse pressures were −0.4 mmHg and −1.5 mmHg (amounting to −0.3 % and −1.4 % of the underlying mean blood pressure).

Copyright © 2016 by ASME

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