Title
Weaning from mechanical ventilation: Feature extraction from a statistical signal processing viewpoint
Abstract
Clinicians' decision for mechanical aid discontinuation is a chal- lenging task that involves a complete knowledge of a great number of clinical parameters, as well as its evolution in time. Respiratory patternvariability appears asa useful extubation readiness indicator, and thus can be used as an informative feature in a statistical pat- tern recognition framework. Reliable assessment of this variability involves a set of signal processing techniques that should be care- fully evaluated for statistical validity. This paper evaluates different variability extraction techniques aimed to build a Bayesian classifier for weaning readiness decision. As a conclusion, Sample Entropy is selected as the best performance extraction method. By calcu- lating it over tidal volume signals, and with mean respiratory rates as additional input patterns, a 2D Bayesian classifier is constructed with principal component analysis selection. The obtained misclas- sification probability (Pe = 0.2141) is acceptable if compared with performance of single feature classifiers.
Year
Venue
Keywords
2005
EUSIPCO
bayes methods,feature extraction,medical signal processing,pattern classification,principal component analysis,2d bayesian classifier,mean respiratory rate,mechanical aid discontinuation,mechanical ventilation,principal component analysis selection,respiratory pattern variability,sample entropy,signal processing technique,single feature classifier,statistical pattern recognition framework,statistical signal processing,tidal volume signal,variability extraction technique,weaning readiness decision
Field
DocType
ISBN
Time series,Signal processing,Sample entropy,Pattern recognition,Naive Bayes classifier,Computer science,Feature extraction,Artificial intelligence,Statistical signal processing,Machine learning,Principal component analysis,Bayes classifier
Conference
978-160-4238-21-1
Citations 
PageRank 
References 
2
0.59
1
Authors
4