Unique parameter identification of a cardiovascular system model using feedback control

in Proc. 7th Intl Conf on Control and Automation (ICCA09) (2009, December)

Robust parameter identification for model-based cardiac diagnosis in critical care

in Proceedings of the 6th IFAC Symposium on Modeling and Control in Biomedical Systems (MCBMS09) (2009)

Model-Based Assessment of Dynamic FRC (DFRC)

in Intensive Care Medicine (2009), 35(suppl. 1), 52

Cardiovascular Modelling and Identification in Septic Shock - Experimental validation

in IFAC 2008 (2008)

Model-based detection of pulmonary embolism using an extended physiologically relevant, cardiovascular model

in Proceedings of Engineering & Physical Sciences in Medicine and Australian Biomedical Engineering Conference (EPSM ABEC 2008) (2008)

Model-based analysis of induced endotoxic shock in pigs with and without hemofiltration,

in Prodeedings of the Engineering & Physical Sciences in Medicine and Australian Biomedical Engineering Conference (EPSM ABEC 2008 (2008)

Improving model-based cardiac diagnosis with an ECG

in Proceedings (CD) of the 4th European Congress for Medical and Biomedical Engineering (eMBEC 2008), Antwerp, Belgium, Nov 23-27, 2008 (2008)

Model-based cardiac diagnosis of pulmonary embolism

in Computer Methods & Programs in Biomedicine (2007), 87(1), 46-60

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification ... [more ▼]

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification methods for this model are highly non-linear and non-convex, hindering real-time clinical application. An integral-based identification method that transforms the problem into a linear, convex problem, has been previously developed, but was only applied on continuous simulated data with random noise. This paper extends the method to handle discrete sets of clinical data, unmodelled dynamics, a significantly reduced data set theta requires only the minimum and maximum values of the pressure in the aorta, pulmonary artery and the volumes in the ventricles. The importance of integrals in the formulation for noise reduction is illustrated by demonstrating instability in the identification using simple derivative-based approaches. The cardiovascular system (CVS) model and parameter identification method are then clinically validated on porcine data for pulmonary embolism. Errors for the identified model are within 10% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents the first clinical validation of these models, methods and approach to cardiovascular diagnosis in critical care. (c) 2007 Elsevier Ireland Ltd. All rights reserved. [less ▲]

Modelling the cardiovascular system

in Critical Care and Resuscitation : Journal of the Australasian Academy of Critical Care Medicine (2007), 9(3), 264-269

Haemodynamic Management Using a Minimal Cardiac Model,” Young Researchers Meeting on Haemodynamic Management

in Pulsion Medical Systems AG (2006)