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dc.contributor.authorSaatçı, Esra
dc.contributor.authorAkan, Aydın
dc.date.accessioned2016-05-31T08:49:53Z
dc.date.available2016-05-31T08:49:53Z
dc.date.issued2010
dc.identifier.issn1687-6172
dc.identifier.urihttp://hdl.handle.net/11413/1353
dc.description.abstractWe 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.tr_TR
dc.language.isoen_UStr_TR
dc.publisherHindawi Publishing Corporation, 410 Park Avenue, 15Th Floor, #287 Pmb, New York, Ny 10022 Usatr_TR
dc.relationEurasip Journal On Advances In Signal Processingtr_TR
dc.subjectparameter-estimationtr_TR
dc.subjectnonlinear modeltr_TR
dc.subjectlungtr_TR
dc.subjectmechanicstr_TR
dc.subjectparametre tahminitr_TR
dc.subjectdoğrusal olmayan modeltr_TR
dc.subjectakciğertr_TR
dc.subjectmekaniktr_TR
dc.titleInverse Modeling of Respiratory System during Noninvasive Ventilation by Maximum Likelihood Estimationtr_TR
dc.typeArticletr_TR
dc.contributor.authorIDTR112197tr_TR
dc.contributor.authorIDTR2918tr_TR
dc.identifier.wos281188900001
dc.identifier.wos281188900001en
dc.identifier.scopus2-s2.0-77956822382
dc.identifier.scopus2-s2.0-77956822382en


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