Estimation Of The Respiratory System Parameters - Sert, Görkem; Akan, Aydın; GÜRKAN, GÜRAY; SAATÇI, ESRA (European Assoc Signal Speech & Image Processing-Eurasip, Po Box 74251, Kessariani, 151 10, Greece, 2011)
In clinical respiratory studies, resistance and the lung compliance are two important respiratory parameters that are often measured by physicians. In this work, Respiratory signals (mask pressure, airway flow, and lung volume) are measured by using artificial lung simulator and mannequin head and respiratory parameters set on the simulator are estimated by the best linear unbiased estimator (BLUE). However, prior to the estimation, muscular pressure signals that symbolize the effect of the respiratory parameters on the respiratory signals are computed by using least mean square (LMS) based adaptive noise canceler (ANC). It is found that LMS filter length considerably effects the filter output and in turn the estimation results. Thus, it is suggested to use mis-adjustement criterion in LMS-ANC filter to select the filter order by processing the signals that have only one respiratory parameter variation. In conclusion, respiratory parameters are successfully estimated from the muscular pressure signals that are filtered out with appropriate filter lengths.