Estimation of respiratory parameters via fuzzy clustering

Abstract

The results of monitoring respiratory parameters estimated from flow-pressure-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 information 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 locally. Parameters of these models are then estimated by least-squares techniques. By analyzing the dependence of these local parameters on the location of the model in the flow-volume-pressure space, information on patients' pulmonary condition can be gained. The effectiveness of the proposed approaches is demonstrated by analyzing the dependence of the expiratory time constant on the volume in patients with chronic obstructive pulmonary disease (COPD) and patients without COPD.

DOI: 10.1016/S0933-3657(00)00075-0

Cite this paper

@article{Babuka2001EstimationOR, title={Estimation of respiratory parameters via fuzzy clustering}, author={R. Babuka and L. Alic and M. S. Lourens and Anton F. M. Verbraak and J. Bogaard}, journal={Artificial intelligence in medicine}, year={2001}, volume={21 1-3}, pages={91-105} }