Estimation of respiratory parameters via fuzzy clustering

The results of monitoring respiratory parameters estimated from flow-pressu re-volume measurements can be used to assess patients' pulmonary condition, to detect poor patient-ventilator interaction and consequently to optimize the ventilator settings. A new method is proposed to obtain detailed infor mation about respiratory parameters without interfering with the expiration . By means of fuzzy clustering, the available data set is partitioned into fuzzy subsets that can be well approximated by linear regression models loc ally. Parameters of these models are then estimated by least-squares techni ques. By analyzing the dependence of these local parameters on the location of the model in the how-volume-pressure space. information on patients' pu lmonary condition can be gained. The effectiveness of the proposed approach es is demonstrated by analyzing the dependence of the expiratory time const ant on the volume in patients with chronic obstructive pulmonary disease (C OPD) and patients without (COPE). (C) 2001 Elsevier Science B.V. All rights reserved.