Estimation of respiratory parameters via fuzzy clustering

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.

[1]  Guido Avanzolini,et al.  A Comparative Evaluation of Three On-Line Identification Methods for Respiratory Mechanical Model , 1985, IEEE Transactions on Biomedical Engineering.

[2]  S. Gottfried,et al.  Noninvasive determination of respiratory system mechanics during mechanical ventilation for acute respiratory failure. , 1985, The American review of respiratory disease.

[3]  D J Lane,et al.  Analysis of expiratory tidal flow patterns as a diagnostic tool in airflow obstruction. , 1998, The European respiratory journal.

[4]  G. Iotti,et al.  Simple method to measure total expiratory time constant based on the passive expiratory flow-volume curve. , 1995, Critical care medicine.

[5]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  K. Cheng,et al.  Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network. , 1996, International journal of bio-medical computing.

[7]  Raghu Krishnapuram,et al.  Fitting an unknown number of lines and planes to image data through compatible cluster merging , 1992, Pattern Recognit..

[8]  D J Lane,et al.  Tidal expiratory flow patterns in airflow obstruction. , 1981, Thorax.

[9]  J. Aerts,et al.  Expiratory flow‐volume curves in mechanically ventilated patients with chronic obstructive pulmonary disease , 1999, Acta anaesthesiologica Scandinavica.

[10]  J. Bates,et al.  Estimation of time-varying respiratory mechanical parameters by recursive least squares. , 1991, Journal of applied physiology.

[11]  Yannis A. Tolias,et al.  A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering , 1998, IEEE Transactions on Medical Imaging.

[12]  F Chabot,et al.  Respiratory mechanics studied by multiple linear regression in unsedated ventilated patients. , 1992, The European respiratory journal.

[13]  Y. Kawata,et al.  Computer-aided diagnosis for pulmonary nodules based on helical CT images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[14]  M. S. Lourens,et al.  Flow-volume curves as measurement of respiratory mechanics during ventilatory support: the effect of the exhalation valve , 1999, Intensive Care Medicine.

[15]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

[16]  H. Ermert,et al.  Comparison of different Neuro-Fuzzy classification systems for the detection of prostate cancer in ultrasonic images , 1997, 1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118).

[17]  J. Milic-Emili,et al.  Interrupter technique for measurement of respiratory mechanics in anesthetized cats. , 1984, Journal of applied physiology: respiratory, environmental and exercise physiology.

[18]  J. Aerts,et al.  Ventilator-CPAP with the Siemens Servo 900C Compared with Continuous Flow-CPAP in Intubated Patients: Effect on Work of Breathing , 1997, Anaesthesia and intensive care.

[19]  R L Somorjai,et al.  Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia. , 1997, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[20]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[21]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[22]  J C Yernault,et al.  Optimal assessment and management of chronic obstructive pulmonary disease (COPD). The European Respiratory Society Task Force. , 1995, The European respiratory journal.

[23]  B.H. Jansen,et al.  A fuzzy clustering approach to EP estimation , 1997, IEEE Transactions on Biomedical Engineering.