• DocumentCode
    3032934
  • Title

    Closed-loop drug infusion using neural networks

  • Author

    Lendl, Markus ; Geldner, Götz ; Unbehauen, Rolf

  • Author_Institution
    Dept. of Theor. Found. of Electr. Eng., Erlangen-Nurnberg Univ., Germany
  • Volume
    5
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    3241
  • Abstract
    Closed-loop systems have been proved as adequate for automated drug administration. They provide significant advantages for the patient as well as for the anaesthetist. In essence three different types of implementing the control unit have been established: the classical linear controller (e.g., PID, adaptive PID), rule-based systems (e.g., fuzzy logic), and model-based controller (e.g., PK/PD models). Major drawbacks of these implementations can be found in the limited flexibility or the expendable procedures for determining appropriate rules and/or model parameters. Our novel approach, applying neural networks in a predictive model-based design, is able to overcome the depicted difficulties. The performance has been verified by clinical investigations using the short acting non-depolarizing muscle relaxants mivacurium. The obtained results encourage further research using hypnotic drugs
  • Keywords
    closed loop systems; drug delivery systems; neurocontrollers; patient monitoring; predictive control; automated drug administration; closed-loop systems; drug infusion; linear controller; model-based controller; muscle relaxants; neural networks; predictive control; pump patient monitoring system; rule-based systems; Adaptive control; Adaptive systems; Automatic control; Drugs; Fuzzy logic; Knowledge based systems; Neural networks; Predictive models; Programmable control; Three-term control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 1999. Proceedings of the 1999
  • Conference_Location
    San Diego, CA
  • ISSN
    0743-1619
  • Print_ISBN
    0-7803-4990-3
  • Type

    conf

  • DOI
    10.1109/ACC.1999.782363
  • Filename
    782363