Abstract
We propose a procedure to estimate the model parameters of presented nonlinear Resistance-Capacitance (RC) and the widely used linear Resistance-Inductance-Capacitance (RIC)models of the respiratory system by Maximum Likelihood Estimator (MLE). The measurement noise is assumed to be Generalized Gaussian Distributed (GGD), and the variance and the shape factor of the measurement noise are estimated by MLE and Kurtosis method, respectively. The performance of the MLE algorithm is also demonstrated by the Cramer-Rao Lower Bound (CRLB) with artificially produced respiratory signals. Airway flow, mask pressure, and lung volume are measured from patients with Chronic Obstructive Pulmonary Disease (COPD) under the noninvasive ventilation and from healthy subjects. Simulations show that respiratory signals from healthy subjects are better represented by the RIC model compared to the nonlinear RC model. On the other hand, the Patient group respiratory signals are fitted to the nonlinear RC model with lower measurement noise variance, better converged measurement noise shape factor, and model parameter tracks. Also, it is observed that for the Patient group the shape factor of the measurement noise converges to values between 1 and 2 whereas for the Control group shape factor values are estimated in the super-Gaussian area.
Highlights
The assessment of the respiratory function is an important part of the clinical medicine [1]
In this paper we introduced a time-domain methodology to determine the respiratory mechanics of Chronic Obstructive Pulmonary Disease (COPD) patients under the non-invasive ventilation based on the inverse system modeling approach
Maximum likelihood estimation implemented by Newton-Raphson algorithm yielded optimum estimators for both the linear RIC and the nonlinear RC models
Summary
The assessment of the respiratory function is an important part of the clinical medicine [1]. (i) The lung is a dynamic system such that its parameters should be monitored continuously even with the ventilatory assistance [2]; (ii) the signals measured at the output of this dynamic system, the input, and the system parameters might be nonlinearly related to each other over one breathing period [2, 3]; and (iii) the proposed methods for investigating the lung mechanics should not require any kind of patient’s cooperation. Using the measured respiratory signals (i.e., airway flow, V (t), and airway pressure (mask pressure), Paw(t)), in the literature, conventional least square (LS) and recursive least square methods were used to estimate the linear and nonlinear model parameters of the respiratory system [4,5,6,7]. It is of interest to choose generalized noise model to express the measurement noise involved in the respiratory system identification problem
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