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Individualized closed-loop control of propofol anesthesia: A preliminary study

Soltesz, Kristian LU ; Hahn, Jin-Oh; Hägglund, Tore LU ; Dumont, Guy A. and Ansermino, J. Mark (2013) In Biomedical Signal Processing and Control 8(6). p.500-508
Abstract
This paper proposes an individualized approach to closed-loop control of depth of hypnosis during propofol anesthesia. The novelty of the paper lies in the individualization of the controller at the end of the induction phase of anesthesia, based on a patient model identified from the dose-response relationship during induction of anesthesia. The proposed approach is shown to be superior to administration of propofol based on population-based infusion schemes tailored to individual patients. This approach has the potential to outperform fully adaptive approaches in regards to controller robustness against measurement variability due to surgical stimulation. To streamline controller synthesis, two output filters were introduced (inverting... (More)
This paper proposes an individualized approach to closed-loop control of depth of hypnosis during propofol anesthesia. The novelty of the paper lies in the individualization of the controller at the end of the induction phase of anesthesia, based on a patient model identified from the dose-response relationship during induction of anesthesia. The proposed approach is shown to be superior to administration of propofol based on population-based infusion schemes tailored to individual patients. This approach has the potential to outperform fully adaptive approaches in regards to controller robustness against measurement variability due to surgical stimulation. To streamline controller synthesis, two output filters were introduced (inverting the Hill dose-response model and the linear time-invariant sensor model), which yield a close-to-linear representation of the system dynamics when used with a compartmental patient model. These filters are especially useful during the induction phase of anesthesia in which a nonlinear dose-response relationship complicates the design of an appropriate controller. The proposed approach was evaluated in simulation on pharmacokinetic and pharmacodynamic models of 44 patients identified from real clinical data. A model of the NeuroSense, a hypnotic depth monitor based on wavelet analysis of EEG, was also included. This monitor is similar to the well-known BIS, but has linear time-invariant dynamics and does not introduce a delay. The proposed scheme was compared with a population-based controller, i.e. a controller only utilizing models based on demographic covariates for its tuning. On average, the proposed approach offered 25 % improvement in disturbance attenuation, measured as the integrated absolute error following a step disturbance. The corresponding standard deviation from the reference was also decreased by 25 %. Results are discussed and possible directions of future work are proposed. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
anesthesia, automatic control, individualized treatment
in
Biomedical Signal Processing and Control
volume
8
issue
6
pages
500 - 508
publisher
Elsevier
external identifiers
  • wos:000329885000004
  • scopus:84878358856
ISSN
1746-8094
DOI
10.1016/j.bspc.2013.04.005
project
LCCC
language
English
LU publication?
yes
id
40f69ef3-97fb-40b2-af5a-881aba7daa9b (old id 3634180)
date added to LUP
2013-04-24 10:59:58
date last changed
2019-05-19 04:01:28
@article{40f69ef3-97fb-40b2-af5a-881aba7daa9b,
  abstract     = {This paper proposes an individualized approach to closed-loop control of depth of hypnosis during propofol anesthesia. The novelty of the paper lies in the individualization of the controller at the end of the induction phase of anesthesia, based on a patient model identified from the dose-response relationship during induction of anesthesia. The proposed approach is shown to be superior to administration of propofol based on population-based infusion schemes tailored to individual patients. This approach has the potential to outperform fully adaptive approaches in regards to controller robustness against measurement variability due to surgical stimulation. To streamline controller synthesis, two output filters were introduced (inverting the Hill dose-response model and the linear time-invariant sensor model), which yield a close-to-linear representation of the system dynamics when used with a compartmental patient model. These filters are especially useful during the induction phase of anesthesia in which a nonlinear dose-response relationship complicates the design of an appropriate controller. The proposed approach was evaluated in simulation on pharmacokinetic and pharmacodynamic models of 44 patients identified from real clinical data. A model of the NeuroSense, a hypnotic depth monitor based on wavelet analysis of EEG, was also included. This monitor is similar to the well-known BIS, but has linear time-invariant dynamics and does not introduce a delay. The proposed scheme was compared with a population-based controller, i.e. a controller only utilizing models based on demographic covariates for its tuning. On average, the proposed approach offered 25 % improvement in disturbance attenuation, measured as the integrated absolute error following a step disturbance. The corresponding standard deviation from the reference was also decreased by 25 %. Results are discussed and possible directions of future work are proposed.},
  author       = {Soltesz, Kristian and Hahn, Jin-Oh and Hägglund, Tore and Dumont, Guy A. and Ansermino, J. Mark},
  issn         = {1746-8094},
  keyword      = {anesthesia,automatic control,individualized treatment},
  language     = {eng},
  number       = {6},
  pages        = {500--508},
  publisher    = {Elsevier},
  series       = {Biomedical Signal Processing and Control},
  title        = {Individualized closed-loop control of propofol anesthesia: A preliminary study},
  url          = {http://dx.doi.org/10.1016/j.bspc.2013.04.005},
  volume       = {8},
  year         = {2013},
}