Nonlinear model for mechanical ventilation of human lungs

Polak, Adam G.; Mroczka, Janusz

doi:10.1016/j.compbiomed.2004.08.001

Cited by 54 publications

(45 citation statements)

References 37 publications

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“…Many respiratory system models were constructed to simulate the interaction between respiratory parameters such as respiratory resistance and airway flow and respiratory compliance and lung volume indirectly [1], [2], [3]. These models were then used to estimate the parameters by applying several methodological approaches [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Lung Model Parameter Estimation by Unscented Kalman Filter

Saatçı

Akan

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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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.

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Section: Introductionmentioning

confidence: 99%

Lung Model Parameter Estimation by Unscented Kalman Filter

Saatçı

Akan

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Elemental equations define the relationship between appropriate pressures and volumetric flow at the specific regions of respiratory system whereas the system state and measurement equations are the mathematical descriptions of the whole system behaviour. Theoretical and experimental studies reveal that respiratory system models may be linear and nonlinear in both state and parameters depending on the system identification technique, considered disease conditions and experimental methodology (Polak & Mroczka, 2006); (Bates & Lutchen, 2005); (Avanzolini et al, 1995). For the interested reader, discussion and comparisons on the linear and nonlinear models can be found in the literature (Diong et al, 2007); (Yuan et al, 1998).…”

Section: Respiratory Modelsmentioning

confidence: 99%

Dual Unscented Kalman Filter and Its Applications to Respiratory System Modelling

Saatçı¹,

Akan²

2009

Kalman Filter Recent Advances and Applications

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“…For lung simulator aim we segment the airway by fluid dynamic performance of different generations. Existed analysis (Polak, 2006) reveals three sections of the bronchial tree with distinct resistive properties. Generations 0-6 are characterized mainly by turbulent dissipation of the head pressure, laminar flow and airways collapsing dominate in generations from 7 to 16 (Mauroy, 2004), and lastly, flow in generations from 17 to 23 is approximately laminar, where compliance, not resistance, is the main parameter.…”

Section: Airway Segment Based On Different Fluid Dynamic Performancementioning

confidence: 99%

“…The airflow values are calculated for quasistatic conditions by solving a system of nonlinear differential equations describing static pressure losses along the airway branches. Then in 2006 he (Polak, 2006) use the nonlinear model to analyze the characteristic of lung mechanical ventilation. In 2008 Adam G. Polak again investigated a model-based method for flow limitation analysis in the heterogeneous human lung.…”

mentioning

confidence: 99%