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Nonlinear Self-Adjointness for some Generalized KdV Equations Read chapter Read first chapter

2014 | OriginalPaper | Chapter

8. Emerging Tools for Quantifying Unconscious Analgesia: Fractional-Order Impedance Models

Authors : Amélie Chevalier, Dana Copot, Clara M. Ionescu, J. A. Tenreiro Machado, Robin De Keyser

Published in: Discontinuity and Complexity in Nonlinear Physical Systems

Publisher: Springer International Publishing

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Abstract

This paper presents the application of model-based predictive control (MPC) in combination with a sensor for the measurement of analgesia (pain relief) in an unconscious patient in order to control the level of anesthesia. The MPC strategy uses fractional-order impedance models (FOIMs) to model the diffusion process that occurs in the human body when an analgesic drug is taken up. Based on this control strategy an early dawn concept of the pain sensor is developed. The grand challenges that coincide with this development include identification of the patient model, validation of the pain sensor, and validation of the effect of the analgesic drug.

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previous chapter Fractional Calculus: From Simple Control Solutions to Complex Implementation Issues
next chapter 1D Cahn–Hilliard Dynamics: Coarsening and Interrupted Coarsening
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Metadata
Title
Emerging Tools for Quantifying Unconscious Analgesia: Fractional-Order Impedance Models
Authors
Amélie Chevalier
Dana Copot
Clara M. Ionescu
J. A. Tenreiro Machado
Robin De Keyser
Copyright Year
2014
Book
Discontinuity and Complexity in Nonlinear Physical Systems

Print ISBN: 978-3-319-01410-4

Electronic ISBN: 978-3-319-01411-1

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-01411-1_8

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