Estimation of respiratory parameters via fuzzy clustering

R. Babuška, L. Alic, M. S. Lourens, A. F.M. Verbraak, J. Bogaard

Research output: Contribution to journalArticle

16 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)91-105
Number of pages15
JournalArtificial Intelligence in Medicine
Volume21
Issue number1-3
DOIs
Publication statusPublished - 1 Jan 2001

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Keywords

  • Expiratory time constant
  • Fuzzy clustering
  • Least-squares estimation
  • Mechanical ventilation
  • Parameter estimation
  • Respiratory mechanics
  • Respiratory resistance and compliance

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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