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Patient specific model based induction of hypnosis using fractional order control

(2017) IFAC PAPERSONLINE. 50(1). p.15097-15102
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Abstract
Optimal and safe control of drug delivery systems with continuous infusion protocol is of key importance to avoid over- or under-dosing of the patient. By implementing close-loops one is able to optimize the amount of drug given to the patient. In this paper a robust control methodology is presented. Emerging tools from fractional calculus have been considered and a fractional order PI controller for drug dosing during hypnosis has been designed. In this paper a robust fractional order control of hypnosis is proposed. The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Keywords
hypnosis, anesthesia modeling and control, personalized healthcare, drug delivery system, robustness, uncertainty

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MLA
Copot, Dana, et al. “Patient Specific Model Based Induction of Hypnosis Using Fractional Order Control.” IFAC PAPERSONLINE, vol. 50, no. 1, 2017, pp. 15097–102, doi:10.1016/j.ifacol.2017.08.2238.
APA
Copot, D., Muresan, C., De Keyser, R., & Ionescu, C.-M. (2017). Patient specific model based induction of hypnosis using fractional order control. IFAC PAPERSONLINE, 50(1), 15097–15102. https://doi.org/10.1016/j.ifacol.2017.08.2238
Chicago author-date
Copot, Dana, Cristina Muresan, Robain De Keyser, and Clara-Mihaela Ionescu. 2017. “Patient Specific Model Based Induction of Hypnosis Using Fractional Order Control.” In IFAC PAPERSONLINE, 50:15097–102. Jul 09-14, 2017. https://doi.org/10.1016/j.ifacol.2017.08.2238.
Chicago author-date (all authors)
Copot, Dana, Cristina Muresan, Robain De Keyser, and Clara-Mihaela Ionescu. 2017. “Patient Specific Model Based Induction of Hypnosis Using Fractional Order Control.” In IFAC PAPERSONLINE, 50:15097–15102. Jul 09-14, 2017. doi:10.1016/j.ifacol.2017.08.2238.
Vancouver
1.
Copot D, Muresan C, De Keyser R, Ionescu C-M. Patient specific model based induction of hypnosis using fractional order control. In: IFAC PAPERSONLINE. Jul 09-14, 2017; 2017. p. 15097–102.
IEEE
[1]
D. Copot, C. Muresan, R. De Keyser, and C.-M. Ionescu, “Patient specific model based induction of hypnosis using fractional order control,” in IFAC PAPERSONLINE, Toulouse, FRANCE, 2017, vol. 50, no. 1, pp. 15097–15102.
@inproceedings{8556263,
  abstract     = {{Optimal and safe control of drug delivery systems with continuous infusion protocol is of key importance to avoid over- or under-dosing of the patient. By implementing close-loops one is able to optimize the amount of drug given to the patient. In this paper a robust control methodology is presented. Emerging tools from fractional calculus have been considered and a fractional order PI controller for drug dosing during hypnosis has been designed. In this paper a robust fractional order control of hypnosis is proposed. The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.}},
  author       = {{Copot, Dana and Muresan, Cristina and De Keyser, Robain and Ionescu, Clara-Mihaela}},
  booktitle    = {{IFAC PAPERSONLINE}},
  issn         = {{2405-8963}},
  keywords     = {{hypnosis,anesthesia modeling and control,personalized healthcare,drug delivery system,robustness,uncertainty}},
  language     = {{eng}},
  location     = {{Toulouse, FRANCE}},
  number       = {{1}},
  pages        = {{15097--15102}},
  title        = {{Patient specific model based induction of hypnosis using fractional order control}},
  url          = {{http://doi.org/10.1016/j.ifacol.2017.08.2238}},
  volume       = {{50}},
  year         = {{2017}},
}

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