Publication:
Lung model parameter estimation by unscented Kalman filter

No Thumbnail Available
Date
2007
Authors
Saatçı, Esra
Akan, Aydın
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Research Projects
Organizational Units
Journal Issue
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.

Description
Keywords
nonlinear model , ventilation , doğrusal olmayan model , havalandırma
Citation