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Research Papers

Model-Based Blind System Identification Approach to Estimation of Central Aortic Blood Pressure Waveform From Noninvasive Diametric Circulatory Signals

[+] Author and Article Information
Zahra Ghasemi

Department of Mechanical Engineering,
University of Maryland,
2107B Glenn L. Martin Hall,
College Park, MD 20742
e-mail: zghasemi@umd.edu

Chang-Sei Kim

Department of Mechanical Engineering,
University of Maryland,
2107B Glenn L. Martin Hall,
College Park, MD 20742
e-mail: cskim75@umd.edu

Eric Ginsberg

Department of Medicine,
University of Maryland Medical Center,
110 South Paca Street, 7th Floor, Cardiology,
Baltimore, MD 21201
e-mail: eginsberg@medicine.umaryland.edu

Anuj Gupta

Department of Medicine,
University of Maryland Medical Center,
110 South Paca Street, 7th Floor, Cardiology,
Baltimore, MD 21201
e-mail: agupta@medicine.umaryland.edu

Jin-Oh Hahn

Mem. ASME
Department of Mechanical Engineering,
University of Maryland,
2181 Glenn L. Martin Hall,
College Park, MD 20742
e-mail: Jhahn12@umd.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 28, 2016; final manuscript received November 29, 2016; published online March 22, 2017. Assoc. Editor: Jongeun Choi.

J. Dyn. Sys., Meas., Control 139(6), 061003 (Mar 22, 2017) (10 pages) Paper No: DS-16-1218; doi: 10.1115/1.4035451 History: Received April 28, 2016; Revised November 29, 2016

This paper presents a model-based blind system identification approach to estimation of central aortic blood pressure (BP) waveform from noninvasive diametric circulatory signals. First, we developed a mathematical model to reproduce the relationship between central aortic BP waveform and a class of noninvasive circulatory signals at diametric locations by combining models to represent wave propagation in the artery, arterial pressure–volume relationship, and mechanics of the measurement instrument. Second, we formulated the problem of estimating central aortic BP waveform from noninvasive diametric circulatory signals into a blind system identification problem. Third, we performed identifiability analysis to show that the mathematical model could be identified and its parameters determined up to an unknown scale. Finally, we illustrated the feasibility of the approach by applying it to estimate central aortic BP waveform from two diametric pulse volume recording (PVR) signals. Experimental results from ten human subjects showed that the proposed approach could estimate central aortic BP waveform accurately: the average root-mean-squared error (RMSE) associated with the central aortic BP waveform was 4.1 mm Hg (amounting to 4.5% of the underlying mean BP) while the average errors associated with central aortic systolic pressure (SP) and pulse pressure (PP) were 2.4 mm Hg and 2.0 mm Hg (amounting to 2.5% and 2.1% of the underlying mean BP). The proposed approach may contribute to the improved monitoring of cardiovascular (CV) health by enabling estimation of central aortic BP waveform from conveniently measurable diametric circulatory signals.

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Figures

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Fig. 1

Mathematical model to reproduce the relationship between central aortic BP and noninvasive diametric circulatory signals. The model is made up of a tube-load model of BP wave propagation and reflection in the artery, a viscoelastic model of the pressure–volume relationship of the arterial wall and tissue, and a mechanistic model of instrument.

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Fig. 2

Mechanistic model of occlusive cuff. Here, the Voigt model is shown as the viscoelastic model of the arterial wall for simplicity of illustration. Pi(t): arterial BP at the distal site i. εi(t): pulsation of the arterial wall at the distal site i. PCi(t): PVR signal at the distal site i. VCi(t): volume of the air in the cuff at the distal site i.

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Fig. 3

Representative example of measured central aortic BP waveform and the same waveform estimated by the model in Eq. (24) equipped with the parameters identified from standard input–output system identification when inputted with (a) arm and (b) leg distal PVR signals

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Fig. 4

Representative example of nonparametric frequency responses (as the surrogates of measured frequency responses) associated with the tube-load and viscoelastic models, as well as the same frequency responses identified from the proposed blind system identification approach associated with (a) arm and (b) leg

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Fig. 5

Representative example of (a) measured arm and leg PVR waveforms, (b) arm and leg BP waveforms estimated by the proposed blind system identification approach and Eq. (26), and (c) measured central aortic BP waveform and the same waveform estimated by the proposed blind system identification approach and Eq. (27)

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