TAILIEUCHUNG - Báo cáo sinh học: " Research Article Inverse Modeling of Respiratory System during Noninvasive Ventilation by Maximum Likelihood Estimation"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Inverse Modeling of Respiratory System during Noninvasive Ventilation by Maximum Likelihood Estimation | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 237562 12 pages doi 2010 237562 Research Article Inverse Modeling of Respiratory System during Noninvasive Ventilation by Maximum Likelihood Estimation Esra Saatci EURASIP Member 1 and Aydin Akan EURASIP Member 2 1 Department of Electronic Engineering Istanbul Kultur University Bakirkoy 34156 Istanbul Turkey 2 Department of Electrical and Electronics Engineering Istanbul University Avcilar 34320 Istanbul Turkey Correspondence should be addressed to Esra Saatci Received 2 October 2009 Revised 25 February 2010 Accepted 31 May 2010 Academic Editor Satya Dharanipragada Copyright 2010 E. Saatci and A. Akan. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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 .

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