Blind Identification of 2-Channel IIR Wave Propagation Systems for Central Cardiovascular Monitoring

Author(s):  
Jin-Oh Hahn ◽  
Andrew T. Reisner ◽  
H. Harry Asada

This paper presents a new approach to blind identification of a class of 2-channel infinite impulse response (IIR) systems describing the wave propagation dynamics. For these systems, this paper derives a blind identifiability condition and develops a blind identification algorithm, which is capable of uniquely determining both the numerator and denominator polynomials of the channel dynamics. The efficacy of the method is illustrated by a 2-sensor central cardiovascular monitoring application as an example, where the cardiovascular blood pressure wave propagation dynamics is identified and the aortic signals are reconstructed from blood pressure measurements at two distinct extremity locations. Experimental results using a swine subject illustrate how the new blind identification approach effectively identifies cardiovascular dynamics and reconstructs the aortic blood pressure and flow signals very accurately from two distinct peripheral blood pressure measurements under diverse physiologic conditions.

Author(s):  
Jin-Oh Hahn ◽  
Andrew T. Reisner ◽  
H. Harry Asada

This paper presents a new approach to blind identification of a class of two-channel infinite impulse response (IIR) systems with applicability to clinical cardiovascular monitoring. Specifically, this paper deals with a class of two-channel IIR systems describing wave propagation dynamics. For this class of systems, this paper first derives a blind identifiability condition and develops a blind identification algorithm, which is able to determine both the numerator and denominator polynomials of the channel dynamics uniquely. This paper also develops a new input signal deconvolution algorithm that can reconstruct the input signal from the identified two-channel dynamics and the associated two-channel measurements. These methods are applied to identify the pressure wave propagation dynamics in the cardiovascular system and reconstruct the aortic blood pressure and flow signals from blood pressure measurements taken at two distinct extremity locations. Persistent excitation, model identifiability, and asymptotic variance are analyzed to quantify the method’s validity, accuracy, and reliability without employing direct measurement of the aortic blood pressure and flow signals. The experimental results based on 83 data segments obtained from a swine subject illustrate how the cardiovascular dynamics can be identified accurately and reliably, and the aortic blood pressure and flow signals can be stably reconstructed from two distinct peripheral blood pressure signals under diverse physiologic conditions.


Author(s):  
Nima Fazeli ◽  
Jin-Oh Hahn

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.


1996 ◽  
Vol 83 (3) ◽  
pp. 665 ◽  
Author(s):  
Nobuhiro Maekawa ◽  
Katsuya Mikawa ◽  
Kahoru Nishina ◽  
Yoshito Kiyonari ◽  
Hidefumi Obara

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