Physiologic-state-adaptive recovery of aortic blood pressure and flow using blind 2-channel IIR cardiovascular system identification

2008 ◽  
Author(s):  
Jin-Oh Hahn ◽  
Andrew Reisner ◽  
H. Harry Asada
Keyword(s):  
Blood Pressure ◽  
System Identification ◽  
Cardiovascular System ◽  
Aortic Blood ◽  
Aortic Blood Pressure ◽  
Adaptive Recovery ◽  
Physiologic State

Active Non-Intrusive System Identification for Cardiovascular Monitoring: Part II — Development of System Identifiction Algorithm

2013 ◽  
Author(s):  
Nima Fazeli ◽  
Jin-Oh Hahn
Keyword(s):  
Blood Pressure ◽  
System Identification ◽  
Cardiovascular System ◽  
Pressure Waves ◽  
Identification Algorithm ◽  
Cardiovascular Monitoring ◽  
Arterial Tree ◽  
Aortic Blood ◽  
Aortic Blood Pressure ◽  
Central Aortic Blood Pressure

In this paper, we present an innovative active non-intrusive system identification approach to cardiovascular monitoring. The proposed approach is based on a dual collocated actuator-sensor system for cardiovascular system identification, in which the actuators actively excite the arterial tree to create rich and informative trans-mural pressure waves traveling in the arterial tree, which are then non-intrusively measured by the collocated sensors. In our previous work, we developed a mathematical model to reproduce the propagation of intra-vascular (arterial) and extra-vascular (artificial) pressure waves along the arterial tree. Then, we used a dual (radial-femoral) blood pressure cuff system as a prototype dual collocated actuator-sensor system to demonstrate the proposed methodological framework to create rich trans-mural pressure waves as well as to non-intrusively reconstruct them from sensor measurements. In this follow-up work, we propose a novel system identification algorithm to derive cardiovascular system dynamics and reconstruct central aortic blood pressure waveform from the trans-mural pressure waves observed at the peripheral locations. It was successfully demonstrated that the system identification algorithm was able to reconstruct the central aortic blood pressure accurately, and that its performance was superior to the passive non-intrusive approach.

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

2016 ◽  
Author(s):  
Zahra Ghasemi ◽  
Chang-Sei Kim ◽  
Eric Ginsberg ◽  
John Duell ◽  
Anuj Gupta ◽  
...  
Keyword(s):  
Blood Pressure ◽  
System Identification ◽  
Cuff Pressure ◽  
Pressure Oscillation ◽  
Pressure Waveform ◽  
Non Invasive ◽  
Aortic Blood ◽  
Blood Pressure Waveform ◽  
Aortic Blood Pressure ◽  
Central Aortic Blood Pressure

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).

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

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Zahra Ghasemi ◽  
Chang-Sei Kim ◽  
Eric Ginsberg ◽  
Anuj Gupta ◽  
Jin-Oh Hahn
Keyword(s):  
Blood Pressure ◽  
Mathematical Model ◽  
System Identification ◽  
Systolic Pressure ◽  
Model Based ◽  
Aortic Blood ◽  
Blind System Identification ◽  
Identification Approach ◽  
Aortic Blood Pressure ◽  
Central Aortic Blood Pressure

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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