By Topic

Machine-learning rule-based fuzzy logic control for depth of anaesthesia

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $33
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
D. A. Linkens ; Sheffield Univ., UK ; J. S. Shieh ; J. E. Peacock

A machine-learning rule-based fuzzy logic controller for depth of anaesthesia which is similar to the way an anaesthetist works is presented in this paper. The results of discussions with anaesthetists to obtain a rule base and the application of fuzzy logic to predict the primary depth of anaesthesia (PDOA) and to control drug administration are very promising. By using simple rules from machine learning trials, similar results for the prediction of PDOA were obtained and can be used to design a drug infusion controller. The robustness of the self-organising fuzzy logic control (SOFLC) algorithm is good and can supplement the anaesthetist's experience for administering drug to patients when the system is dynamic and time-varying. Using these results, the design of a hierarchical architecture for the determination of the level of depth of anaesthesia is being investigated, which will include the use of clinical signs and refinements in the control of drug administered to patients.

Published in:

Control, 1994. Control '94. International Conference on  (Volume:1 )

Date of Conference:

21-24 March 1994