Skip to main content

Advertisement

SpringerLink
Log in

A delayed functional observer/predictor with bounded-error for depth of hypnosis monitoring

  • Original Research
  • Published:
Journal of Clinical Monitoring and Computing Aims and scope Submit manuscript

Abstract

With the motivation of providing safety for a patient under anesthesia, this paper suggests conditions for evaluating the correctness of an available user interface for systems under shared control based on observability and predictability requirements. Situation awareness is necessary for the user to make correct decisions about the inputs. In this article, we develop a technique to investigate the conditions under which an anesthetists can attain situation awareness about a limited but important aspect of anesthesia, namely depth of hypnosis (DOH). Furthermore, we consider that, in practice, to attain situation awareness, the estimation of the task states does not necessarily need to be precise but can be bounded within certain margins. Hence, attaining situation awareness about DOH is modeled as a bounded-error delayed functional observation/prediction. Unless such an observer/predictor exists for a system with a given user-interface, the safety of the operation may be compromised. The suggested technique proves that, in order to provide safety for the patient under anesthesia, it is necessary for the anesthetist to have access to the predictive information from a clinical decision support system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Absalom A, Kenny G. paedfusor pharmacokinetic data set. Br J Anaesth. 2005;95(1):110.

    Article  CAS  PubMed  Google Scholar 

  2. Awh E, Vogel E, Oh SH. Interactions between attention and working memory. Neuroscience. 2006;139(1):201–8.

    Article  CAS  PubMed  Google Scholar 

  3. Billings CE. Aviation automation: the search for a human-centered approach. Mahwah, NJ: Lawrence Erlbaum Associates; 1997.

  4. Bowman DA, Kruijff E, LaViola JJ Jr, Poupyrev I. An introduction to 3-d user interface design. Presence Teleoperators Virtual Environ. 2001;10(1):96–108.

    Article  Google Scholar 

  5. Carretti B, Cornoldi C, De Beni R, Romanò M. Updating in working memory: a comparison of good and poor comprehenders. J Exp Child Psychol. 2005;91(1):45–66.

    Article  PubMed  Google Scholar 

  6. Cowan N. What are the differences between long-term, short-term, and working memory? Prog Brain Res. 2008;169:323–38.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Darouach M. Existence and design of functional observers for linear systems. IEEE Trans Autom Control. 2000;45(5):940–3.

    Article  Google Scholar 

  8. Darouach M. Functional observers for systems with unknown inputs. In: Proceedings of the 16th international symposium on mathematical theory of networks and systems. Belgium: Leuven; 2004.

  9. Darouach M. On the functional observers for linear descriptor systems. Syst Control Lett. 2012;61(3):427–34.

    Article  Google Scholar 

  10. Endsley M. Toward a theory of situation awareness in dynamic systems. Hum Factors. 1995;37:32–64.

    Article  Google Scholar 

  11. Endsley MR. Theoretical underpinnings of situation awareness: a critical review. In: Situation awareness analysis and measurement. 2000. p. 3–32.

  12. Endsley MR. Designing for situation awareness: an approach to user-centered design. US: Taylor & Francis; 2003.

    Book  Google Scholar 

  13. Endsley MR. Designing for situation awareness: An approach to user-centered design. CRC Press; 2016.

  14. Eskandari N, Dumont GA, Wang ZJ. Delay-incorporating observability and predictability analysis of safety-critical continuous-time systems. IET Control Theory Appl. 2015;9(11):1692–9.

    Article  Google Scholar 

  15. Eskandari N, Dumont GA, Wang ZJ. An observer/predictor-based model of the user for attaining situation awareness. IEEE Trans Hum Mach Syst. 2016;46(2):279–90.

    Article  Google Scholar 

  16. Eskandari N, Oishi M. Computing observable and predictable subspaces to evaluate user-interfaces of lti systems under shared control. In: Proceedings of the IEEE conference on systems, man and cybernetics, pp. 2803–2808. Alaska, USA 2011.

  17. Eskandari N, Wang Z, Dumont G. On the existence and design of functional observers for LTI systems, with application to user modeling. Asian J Control. 2014;18(1):192–205.

    Article  Google Scholar 

  18. Eskandari N, Wang ZJ, Dumont G. Modeling the user as an observer to determine display information requirements. In: 2013 IEEE international conference on systems, man, and cybernetics (SMC), pp. 267–272. IEEE 2013.

  19. Fernando T, MacDougall S, Sreeram V, Trinh H. Existence conditions for unknown input functional observers. Int J Control. 2013;86(1):22–8.

    Article  Google Scholar 

  20. Fernando T, Trinh H. A system decomposition approach to the design of functional observers. Int J Control. 2014;87(9):1846–60.

    Article  Google Scholar 

  21. Fernando T, Trinh HM, Jennings L. Functional observability and the design of minimum order linear functional observers. IEEE Trans Autom Control. 2010;55(5):1268–73.

    Article  Google Scholar 

  22. Fioratou E, Flin R, Glavin R, Patey R. Beyond monitoring: distributed situation awareness in anaesthesia. Br J Anaesth. 2010;105(1):83–90.

    Article  CAS  PubMed  Google Scholar 

  23. Gaba DM, Howard SK, Small SD. Situation awareness in anesthesiology. Hum Factors J Hum Factors Ergon Soc. 1995;37(1):20–31.

    Article  CAS  Google Scholar 

  24. Gin T. Clinical pharmacology on display. Anesth Analg. 2010;111(2):256–8.

    Article  PubMed  Google Scholar 

  25. Hahn JO, Dumont GA, Ansermino JM. Observer-based strategies for anesthesia drug concentration estimation. INTECH Open Access Publisher; 2012.

  26. Hahn JO, Dumont GA, Ansermino JM. Closed-loop anesthetic drug concentration estimation using clinical-effect feedback. IEEE Trans Biomed Eng. 2011;58(1):3–6.

    Article  PubMed  Google Scholar 

  27. Hahn JO, Khosravi S, Dosani M, Dumont GA, Ansermino JM. Pharmacodynamic modeling of propofol-induced tidal volume depression in children. J Clin Monit Comput. 2011;25(4):275–84.

    Article  PubMed  Google Scholar 

  28. van Heusden K, Ansermino JM, Soltesz K, Khosravi S, West N, Dumont GA. Quantification of the variability in response to propofol administration in children. IEEE Trans Biomed Eng. 2013;60(9):2521–9.

    Article  PubMed  Google Scholar 

  29. Hou M, Pugh A, Muller P. Disturbance decoupled functional observers. IEEE Trans Autom Control. 1999;44(2):382–6.

    Article  Google Scholar 

  30. Jennings L, Fernando T, Trinh H. Existence conditions for functional observability from an eigenspace perspective. IEEE Trans Autom Control. 2011;56(12):2957–61.

    Article  Google Scholar 

  31. Kalman RE. A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng. 1960;82(Series D):35–45.

    Article  Google Scholar 

  32. Luenberger D. An introduction to observers. IEEE Trans Autom Control. 1971;16(6):596–602.

    Article  Google Scholar 

  33. Michels P, Gravenstein D, Westenskow DR. An integrated graphic data display improves detection and identification of critical events during anesthesia. J Clin Monit. 1997;13(4):249–59.

    Article  CAS  PubMed  Google Scholar 

  34. Murdoch P. Observer design for a linear functional of the state vector. IEEE Trans Autom Control. 1973;18(3):308–10.

    Article  Google Scholar 

  35. Nam P, Pathirana P, Trinh H. \(\varepsilon\)-bounded state estimation for time-delay systems with bounded disturbances. Int J Control. 2014;87(9):1747–56.

    Article  Google Scholar 

  36. Nassreddine G, Abdallah F, Denoux T. State estimation using interval analysis and belief-function theory: application to dynamic vehicle localization. IEEE Trans Syst Man Cybern Part B Cybern. 2010;40(5):1205–18.

    Article  Google Scholar 

  37. Nguyen M, Trinh H, Nam P. Linear functional observers with guaranteed \(\varepsilon\)-convergence for discrete time-delay systems with input/output disturbances. Int J Syst Sci. 2016;47(13):3193–205.

    Article  Google Scholar 

  38. Parasuraman R, Sheridan TB, Wickens CD. A model for types and levels of human interaction with automation. IEEE Trans Syst Man Cybern Part A Syst Hum. 2000;30(3):286–97.

    Article  CAS  Google Scholar 

  39. Pasolunghi MC, Cornoldi C, De Liberto S. Working memory and intrusions of irrelevant information in a group of specific poor problem solvers. Mem Cogn. 1999;27(5):779–90.

    Article  Google Scholar 

  40. Schulz CM, Endsley MR, Kochs EF, Gelb AW, Wagner KJ. Situation awareness in anesthesia: concept and research. Anesthesiology. 2013;118(3):729–42.

    Article  PubMed  Google Scholar 

  41. Schüttler J, Ihmsen H. Population pharmacokinetics of propofol: a multicenter study. Anesthesiology. 2000;92(3):727–38.

    Article  PubMed  Google Scholar 

  42. Seignez E, Kieffer M, Lambert A, Walter E, Maurin T. Real-time bounded-error state estimation for vehicle tracking. Int J Robot Res. 2009;28(1):34–48.

    Article  Google Scholar 

  43. Shneiderman B. Designing the user interface. India: Pearson Education; 2003.

    Google Scholar 

  44. Skogestad S, Postlethwaite I. Multivariable feedback control: analysis and design, vol. 2. New York: Wiley; 2007.

    Google Scholar 

  45. Trinh H, Fernando T, Nahavandi S. Design of reduced-order functional observers for linear systems with unknown inputs. Asian J Control. 2004;6(4):514–20.

    Article  Google Scholar 

  46. Tsui CC. A new algorithm for the design of multifunctional observers. IEEE Trans Autom Control. 1985;30(1):89–93.

    Article  Google Scholar 

  47. Welch G, Bishop G. An introduction to the kalman filter. University of North Carolina, Department of Computer Science. TR 95-041; 1995.

Download references

Acknowledgments

This study was funded by NSERC Discovery Grant (NSERC RGPIN 157106-13).

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada

    Neda Eskandari, Z. Jane Wang & Guy A. Dumont

Authors
  1. Neda Eskandari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Z. Jane Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guy A. Dumont
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neda Eskandari.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eskandari, N., Wang, Z.J. & Dumont, G.A. A delayed functional observer/predictor with bounded-error for depth of hypnosis monitoring. J Clin Monit Comput 31, 1043–1052 (2017). https://doi.org/10.1007/s10877-016-9929-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10877-016-9929-2

Keywords

Search

Navigation