Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Adaptive ventilator Fi02 advisor: use of non-invasive estimations of shunt
Artificial Intelligence in Medicine
Design of RBF network based on fuzzy clustering method for modeling of respiratory system
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis
Artificial Intelligence in Medicine
| Hi-index | 0.00 |
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.