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    Event-Based control of depth of hypnosis in anesthesia

    253777.pdf (14.47Mb)
    Access Status
    Open access
    Authors
    Merigo, L.
    Beschi, M.
    Padula, Fabrizio
    Latronico, N.
    Paltenghi, M.
    Visioli, A.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Merigo, L. and Beschi, M. and Padula, F. and Latronico, N. and Paltenghi, M. and Visioli, A. 2017. Event-Based control of depth of hypnosis in anesthesia. Computer Methods and Programs in Biomedicine. 147: pp. 63-83.
    Source Title
    Computer Methods and Programs in Biomedicine
    DOI
    10.1016/j.cmpb.2017.06.007
    ISSN
    0169-2607
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/54189
    Collection
    • Curtin Research Publications
    Abstract

    Background and Objective: In this paper, we propose the use of an event-based control strategy for the closed-loop control of the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. Methods: A new event generator with high noise-filtering properties is employed in addition to a PIDPlus controller. The tuning of the parameters is performed off-line by using genetic algorithms by considering a given data set of patients. Results: The effectiveness and robustness of the method is verified in simulation by implementing a Monte Carlo method to address the intra-patient and inter-patient variability. A comparison with a standard PID control structure shows that the event-based control system achieves a reduction of the total variation of the manipulated variable of 93% in the induction phase and of 95% in the maintenance phase. Conclusions: The use of event based automatic control in anesthesia yields a fast induction phase with bounded overshoot and an acceptable disturbance rejection. A comparison with a standard PID control structure shows that the technique effectively mimics the behavior of the anesthesiologist by providing a significant decrement of the total variation of the manipulated variable.

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