Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms

  • Authors:

  • Mahdi Mahfouf;Catarina S. Nunes;Derek A. Linkens;John E. Peacock

  • Affiliations:

  • Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;Departamento de Matemática Aplicada, Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal;Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;Department of Anaesthesia, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, UK

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2005

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Abstract

Objective: Part II of this research study is concerned with the development of a closed-loop simulation linking the patient model as well as the fuzzy relational classifier already introduced in Part I with a control algorithm. The overall architecture is in fact a system advisor, which provides information to the anaesthetist about the adequate infusion-rates of propofol and remifentanil simultaneously. Methods and material: The developed fuzzy multivariable controller includes three rule-bases and takes into account the synergetic interactions between the above drugs and uses such knowledge to achieve rapidly the desired depth of anaesthesia (DOA) level. Results: The result of the study is a closed-loop control scheme, which adjusts efficiently the infusion-rates of two drugs in response to DOA changes. This controller can either be used in an advisory mode or closed-loop feedback mode in the operating theatre during surgery. Conclusion: It is hoped that this control scheme coupled with the patient model presented in Part I of this study will be used routinely in the operating theatre in the very near future. re.