Title :
Lung Model Parameter Estimation by Unscented Kalman Filter
Author :
Saatci, E. ; Akan, A.
Author_Institution :
Istanbul Kultur Univ., Istanbul
Abstract :
Dynamic nonlinear models are the best choice to analyze respiratory systems and to describe system mechanics. In this work, unscented Kalman filtering (UKF) was used to estimate the dynamic nonlinear model parameters of the lung model by using the measured airway flow, mask pressure and integrated lung volume. Artificially generated data and the data from chronic obstructive pulmonary diseased (COPD) patients were analyzed by the proposed model and the proposed UKF algorithm. Simulation results for both cases demonstrated that UKF is a promising estimation method for the respiratory system analysis.
Keywords :
Kalman filters; biomedical measurement; diseases; lung; medical computing; pneumodynamics; airway flow measurement; chronic obstructive pulmonary diseased patients; dynamic nonlinear models; lung model parameter estimation; lung volume; mask pressure; respiratory system analysis; unscented Kalman filter; Algorithm design and analysis; Filtering; Fluid flow measurement; Kalman filters; Lungs; Nonlinear dynamical systems; Parameter estimation; Pressure measurement; Respiratory system; Volume measurement; Computer Simulation; Humans; Lung; Models, Biological; Nonlinear Dynamics; Pulmonary Disease, Chronic Obstructive; Respiratory Mechanics; Software;
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
Print_ISBN :
978-1-4244-0787-3
DOI :
10.1109/IEMBS.2007.4352850
