title: Lung model parameter estimation by unscented Kalman filter.
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.
authors.affiliation: Department of Electronic Engineering, Istanbul Kültür University, Bakirkoy, Istanbul, Türkiye. esra.saatci@iku.edu.tr
authors.forename: Esra
authors.initials: E
authors.lastname: Saatçi
authors.forename: Aydin
authors.initials: A
authors.lastname: Akan
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008168:Lung / Q000502:physiology*; D008954:Models, Biological; D017711:Nonlinear Dynamics; D029424:Pulmonary Disease, Chronic Obstructive / Q000503:physiopathology; D015656:Respiratory Mechanics / Q000502:physiology*; D012984:Software