Linked Life Data

10356301

Source:http://linkedlifedata.com/resource/pubmed/id/10356301

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
1999-7-30
pubmed:abstractText
Self-learning fuzzy logic control has the important property of accommodating uncertain, nonlinear, and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time without the need for detailed process modeling. In this study we utilize temporal knowledge of generated rules to improve control performance. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block of patients in the operating theater where the patient response exhibits high nonlinearity and individual patient dose requirements may vary fivefold during an operating procedure. We developed a computer control system utilizing Relaxograph (Datex) measurements to assess the clinical performance of a self-learning fuzzy controller in this application. Using a T1 setpoint of 10% of baseline in 10 patients undergoing general surgery, we found a mean T1 error of 0.28% (SD = 0.39%) while accommodating a 0.25 to 0.38 mg/kg/h range in the mean atracurium infusion rate. This result compares favorably with more complex and computationally intensive model-based control strategies for atracurium infusion.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0010-4809
pubmed:author
pubmed:copyrightInfo
Copyright 1999 Academic Press.
pubmed:issnType
Print
pubmed:volume
32
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
187-97
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Self-learning fuzzy control with temporal knowledge for atracurium-induced neuromuscular block during surgery.
pubmed:affiliation
Department of Automatic Control and Systems Engineering, University of Sheffield, South Yorkshire S1 3JD, Sheffield, United Kingdom.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't