Application of the Unscented Kalman Filter to parameter estimation in biomedical systems
2019-01-24
Application of the Unscented Kalman Filter to parameter estimation in biomedical systems
Patient-specific computational modelling of biomedical systems requires estimation of a large number of model parameters. This estimation is typically performed through clinical measurements, which are inherently uncertain, acquired in the patient. In recent years Kalman-filtering type methods, which can be viewed as recursive Bayesian methods or data assimilation techniques, have gained popularity for this purpose. This talk will present the formulation and application of such methods. Particular focus will on the the widely used Unscented Kalman Filter, which provides computational efficiency. Applications will be presented on both lumped-parameter and geometric multi-scale models of haemodynamics. Two pathophysiologies of congenital heart disease will be of concern: i) coarctation of the aorta; and ii) hypoplastic left heart syndrome (single-ventricle physiology).
