Adaptive control of left ventricular bypass assist devices

McInnis, B.; Guo, Zhong-Wei; Lu, Po‐Chien; Wang, Jiun-Chung

doi:10.1109/tac.1985.1103950

Cited by 57 publications

(15 citation statements)

References 14 publications

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“…The values used in our model have been experimentally defined, by the comparison between simulated signals and physiological signals. In this work we have used, for the systemic circulation that is constituted of a single loop, the parameter values of McInnis et al (1985), except for the vena cava that comes from Olufsen et al (2005). For the other version of the model of systemic circulation, the aorta parameter values has been obtained from McInnis et al (1985), the arteries values come from Olufsen et al (2005) and the venous values are taken from Heldt et al (2002).…”

Section: Model Of the Circulatory Systemmentioning

confidence: 99%

“…For the other version of the model of systemic circulation, the aorta parameter values has been obtained from McInnis et al (1985), the arteries values come from Olufsen et al (2005) and the venous values are taken from Heldt et al (2002). For both models, the parameters of the pulmonary circulation model are those of McInnis et al (1985). For example, the values of the parameters of the aorta are: C = 0.2199 ml/mmHg, R = 0.0675 mmHg.s/ml and I = 0.000825 mmHg.s/sml.…”

Section: Model Of the Circulatory Systemmentioning

confidence: 99%

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A Bond Graph Model of the Cardiovascular System

et al. 2005

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The study of the autonomic nervous system (ANS) function has shown to provide useful indicators for risk stratification and early detection on a variety of cardiovascular pathologies. However, data gathered during different tests of the ANS are difficult to analyse, mainly due to the complex mechanisms involved in the autonomic regulation of the cardiovascular system (CVS). Although model-based analysis of ANS data has been already proposed as a way to cope with this complexity, only a few models coupling the main elements involved have been presented in the literature. In this paper, a new model of the CVS, representing the ventricles, the circulatory system and the regulation of the CVS activity by the ANS, is presented. The models of the vascular system and the ventricular activity have been developed using the Bond Graph formalism, as it proposes a unified representation for all energetic domains, facilitating the integration of mechanic and hydraulic phenomena. In order to take into account the electro-mechanical behaviour of both ventricles, an electrophysiologic model of the cardiac action potential, represented by a set of ordinary differential equations, has been integrated. The short-term ANS regulation of heart rate, cardiac contractility and peripheral vasoconstriction is represented by means of continuous transfer functions. These models, represented in different continuous formalisms, are coupled by using a multi-formalism simulation library. Results are presented for two different autonomic tests, namely the Tilt Test and the Valsalva Manoeuvre, by comparing real and simulated signals.

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Section: Model Of the Circulatory Systemmentioning

confidence: 99%

Section: Model Of the Circulatory Systemmentioning

confidence: 99%

A Bond Graph Model of the Cardiovascular System

et al. 2005

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“…Also, prior approaches have mostly been applied to LPN layouts with few elements or have made use of a priori information to significantly reduce the number of parameters. Finally, although many studies in the literature (see, e.g., [15, 17, 20–24]) employ a Bayesian representation of the unknown parameters through a vector of random variables, they often aim primarily to determine optimal parameter estimates, without deriving associated confidence metrics.…”

Section: Introductionmentioning

confidence: 99%

Patient‐specific parameter estimation in single‐ventricle lumped circulation models under uncertainty

Schiavazzi

Baretta

Pennati

et al. 2016

Numer Methods Biomed Eng

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Summary Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology.

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“…Similar systems can also be assembled by coupling 3D CFD models with 1D peripheral counterparts. While techniques for parameter estimation and data assimilation in lumped circulation networks have been proposed in the literature (see, e.g., Spilker and Taylor, 2010, Revie et al, 2013, Sughimoto et al, 2013, Yu et al, 2001, Xiao et al, 2002, Deswysen, 1977, Clark et al, 1980, Deswysen et al, 1980, McInnis et al, 1985, Shimooka et al, 1991, Avanzolini et al, 1992, Ruchti et al, 1993, Yu et al, 1998), the computational cost of solving the coupled 0D/3D problem has prevented extension of these techniques to multiscale models.…”

Section: Introductionmentioning

confidence: 99%

On a sparse pressure-flow rate condensation of rigid circulation models

Schiavazzi

Hsia

Marsden

2016

Journal of Biomechanics

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Cardiovascular simulation has shown potential value in clinical decision-making, providing a framework to assess changes in hemodynamics produced by physiological and surgical alterations. State-of-the-art predictions are provided by deterministic multiscale numerical approaches coupling 3D finite element Navier Stokes simulations to lumped parameter circulation models governed by ODEs. Development of next-generation stochastic multiscale models whose parameters can be learned from available clinical data under uncertainty constitutes a research challenge made more difficult by the high computational cost typically associated with the solution of these models. We present a methodology for constructing reduced representations that condense the behavior of 3D anatomical models using outlet pressure-flow polynomial surrogates, based on multiscale model solutions spanning several heart cycles. Relevance vector machine regression is compared with maximum likelihood estimation, showing that sparse pressure/flow rate approximations offer superior performance in producing working surrogate models to be included in lumped circulation networks. Sensitivities of outlets flow rates are also quantified through a Sobol’ decomposition of their total variance encoded in the orthogonal polynomial expansion. Finally, we show that augmented lumped parameter models including the proposed surrogates accurately reproduce the response of multiscale models they were derived from. In particular, results are presented for models of the coronary circulation with closed loop boundary conditions and the abdominal aorta with open loop boundary conditions.

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Adaptive control of left ventricular bypass assist devices

Cited by 57 publications

References 14 publications

A Bond Graph Model of the Cardiovascular System

A Bond Graph Model of the Cardiovascular System

Patient‐specific parameter estimation in single‐ventricle lumped circulation models under uncertainty

On a sparse pressure-flow rate condensation of rigid circulation models

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