Modeling and 2-sensor blind identification of human cardiovascular system
Introduction
Among many signals of interest in the cardiovascular (CV) system, the central signals, such as the aortic blood pressure (BP) and flow (BF), are important to understand cardiac physiology and global circulation, in comparison with extremity signals. Peripheral circulatory measurements, e.g. arterial BP measured at distal extremity locations, cannot be used as direct surrogates for their central counterparts, because the morphology of the central CV signals are distorted in distal locations (Nichols & O’Rourke, 1998). However, direct measurement of central signals, such as pulmonary artery or central aortic catheterization, entails costly and risky invasive procedures.
Therefore, there has been substantial interest in developing indirect methods to recover central CV signals from peripheral measurements by utilizing the dynamic relationship between the two, i.e. the CV dynamics. Most of the methods available to date involve a population-based model, employing parameter values derived from prior experimentation (Fetics, Nevo, Chen, & Kass, 1999; Gallagher, Adji, & O’Rourke, 2004; Hope, Tay, Meredith, & Cameron, 2003; Stok, Westerhof, & Karemaker, 2006). In many cases, this approach yields useful estimates (Fetics et al., 1999; Gallagher et al., 2004). In theory, however, the use of a population-based model for different subjects under diverse physiologic conditions will be invalid to some extent, i.e. there will be states when an average relationship between the aortic and peripheral signals is less applicable (Hope et al., 2003; Stok et al., 2006).
In order to reduce the reliance on a priori knowledge and population-based assumptions regarding the relationship between central and extremity CV signals, efforts have been made to use multiple extremity circulatory measurements (Hahn, Reisner, & Asada (2006), Hahn, Reisner, & Asada (2009); Swamy, Ling, Li, & Mukkamala, 2007; Zhang & Asada, 2004). It has been shown that, having sensors placed at multiple peripheral locations to measure BP waves, clinically important signals and phenomena may be monitored by identifying its dynamics using the multi-channel blind system identification (ID) methodology (Gurelli & Nikias, 1995; Xu, Liu, Tong, & Kailath, 1995).
Some of these efforts are based on the black-box models (Swamy et al., 2007; Zhang and Asada, 2004), where the order of the models has to be manually tuned by trial and error for different physiologic conditions to obtain high-fidelity models. To overcome this drawback, the feasibility of a physics-based approach was developed and demonstrated (Hahn, Reisner, & Asada (2006), Hahn, Reisner, & Asada (2009)), which was successfully applied to experimental swine subjects. In an attempt to extend its initial success to human CV health monitoring, this paper develops a physics-based model of human CV dynamics, and presents a method for its ID based on two distinct extremity BP measurements. The validity of the model and the approach will be illustrated using the central aortic and the peripheral BP data obtained from a realistic human CV system simulator (Ozawa, Bottom, Xiao, & Kamm, 2001). Theoretical analysis addressing the blind identifiability provides a sound basis for evaluating and validating the proposed method, altogether yielding an accurate and reliable method for characterizing the human CV dynamics.
This paper is organized as follows. Section 2 develops a physics-based model of human CV system. Blind ID method for a class of two-channel Wiener systems is presented and analyzed in Section 3, which is applied to characterize the human CV system model developed in Section 2. Section 4 discusses the human CV system simulator data used to validate the proposed method and the results obtained based on those data. Conclusion and future work are given in Section 5.
Section snippets
Modeling of two-channel human cardiovascular system
In this paper, the CV system is viewed as a two-channel wave propagation system, where its upper- and the lower-limb branches are described by distinct wave propagation channels in which BP and BF waves travel back and forth. In this context, the use of lumped parameter models such as the Windkessel model (Fogliardi, Di Donfrancesco, & Burattini, 1996) and the chamber-based model (Smith, Geoffrey Chase, Shaw, & Nokes, 2005) is not relevant due to the nonzero wave propagation time, which can
Blind ID of two-channel human cardiovascular system
In this section, a blind ID method for a class of two-channel Wiener systems (which subsumes the human CV system model developed in this paper) is presented, and this method is applied to identify the human CV system model and reconstruct the unknown aortic BP.
Human cardiovascular system simulator
A realistic human CV system simulator was used to investigate the validity of the human CV system model and its ID method presented in this paper. In particular, a distributed CV system model developed by Ozawa et al. (2001) was used in order to create BP data for several human subject models under different physiologic conditions. This model consists of a distributed arterial system and boundary conditions to simulate the left ventricle, bifurcations and peripheral vessels. The CV system model
Conclusion
In this paper, a method to characterize the human CV system and reconstruct the central/abdominal aortic BP for indirect central CV health monitoring applications was presented. A physics-based model of the two-channel human CV system was developed, which, using the blind ID method for two-channel Wiener systems presented in this paper, can be characterized from the observation of arterial BP at two distinct extremity locations. Using the eight human subject models based on a realistic human CV
Acknowledgments
This research was supported in part by the Sharp Corporation. We also appreciate Professor Roger D. Kamm and Dr. Xinshu Xiao at Massachusetts Institute of Technology for their kind assistance and suggestions in the use of the human CV system simulator.
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