Title :
Modeling Human Respiratory Impedance
Author :
Diong, Bill ; Nazeran, H. ; Nava, P. ; Goldman, M.
Author_Institution :
Texas Univ., El Paso, TX
Abstract :
This article describes the analysis of a new respiratory system model that could have properties favorable for disease detection, diagnosis, and treatment. First, we compare the performance of four well-known models to the performance of this new model by estimating their parameters and calculating the corresponding estimation errors. Next, this proposed extended RIC model´s parameter estimates for ill and healthy subjects´ data are compared to gauge their ability to discriminate between these groups. In addition, we present an analysis using this model that supports the observed strong correlation between the frequency-dependence of respiratory resistance at low frequencies with the magnitude of the low-frequency reactance area
Keywords :
acoustic impedance; biomedical measurement; diseases; lung; measurement errors; pneumodynamics; RIC model parameter estimation; disease detection; disease diagnosis; disease treatment; healthy subjects data; human respiratory impedance model; ill subjects data; least estimation errors; respiratory resistance; Biological system modeling; Elasticity; Frequency; H infinity control; Humans; Immune system; Impedance measurement; Lungs; Predictive models; Viscosity; Computer Simulation; Elasticity; Female; Humans; Least-Squares Analysis; Lung Compliance; Lung Diseases, Obstructive; Male; Middle Aged; Models, Biological; Plethysmography, Impedance; Viscosity;
Journal_Title :
Engineering in Medicine and Biology Magazine, IEEE
DOI :
10.1109/MEMB.2007.289121