Advertisement

Recurrence Quantification Analysis of EEG Predicts Responses to Incision During Anesthesia

  • Liyu Huang
  • Weirong Wang
  • Sekou Singare
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

The need for assessing the depth of anesthesia during surgical operations has existed since the introduction of anaesthesia, but sufficiently reliable method is still not found. This paper presents a new approach to detect depth of anaesthesia by using recurrence quantification analysis of electroencephalogram (EEG) and artificial neural network(ANN). The EEG recordings were collected from consenting patient prior to incision during isoflurane anaesthesia of different levels. The four measures of recurrence plot were extracted from each of eight-channel EEG time series. Prediction was made by means of ANN. The system was able to correctly classify purposeful responses in average accuracy of 87.76% of the cases.

Keywords

Artificial Neural Network Recurrence Plot Recurrence Quantification Analysis Recurrence Point Bispectral Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Moerman, N., Bonke, B., Oosting, J.: Awareness and recall during general anesthesia: facts and feelings. Anesthesiology 79, 454–464 (1993)CrossRefGoogle Scholar
  2. 2.
    Ghoneim, M.M., Block, R.L.: Learning and consciousness during general anaesthesia. Anesthesiology 76, 279–305 (1992)CrossRefGoogle Scholar
  3. 3.
    Rubin, M.A., Freeman, H.: Brain potential changes in man during cyclopropane anesthesia. J. Neurophysiol. 3, 33–42 (1940)Google Scholar
  4. 4.
    Thomsen, C.E., Christensen, K.N., Rosenflack, A.: Computerized monitoring of depth of anaesthesia with isoflurane. Br. J. Anaesthesia 63, 36–43 (1989)CrossRefGoogle Scholar
  5. 5.
    Sharma, A., Roy, R.J.: Design of a recognition system to predict movement during anaesthesia. IEEE Trans. on Biomed. Eng. 44, 505–511 (1997)CrossRefGoogle Scholar
  6. 6.
    Kearse, L.A., Manberg, P., DeBros, F. (eds.): Bispectral analysis of the electroencephalo-gram during induction of anesthesia predict hemodynamic responses to laryngoscopy and intubation. Electroencephalography and clinical Neurophysiology 90, 194–200 (1994)CrossRefGoogle Scholar
  7. 7.
    Vernon, J.M., Lang, E., Sebel, P.S.: Prediction of movement using bispectral electroencephalographic analysis during propofol/alfentanil or isoflurane/alfentanil anesthesia. Anesth. Analg. 80, 780–785 (1995)CrossRefGoogle Scholar
  8. 8.
    Nayak, A., Roy, R.J., Sharma, A.: Time-frequency spectral representation of the EEG as an aid in the detection of depth of anaesthesia. Ann. Biomed. Eng. 22, 501–513 (1994)CrossRefGoogle Scholar
  9. 9.
    Eckmann, J.P., Kamphorst, S.O., Ruelle, D.: Recurrence plots of dynamical systems. Europhys. Lett. 4, 973–977 (1987)CrossRefGoogle Scholar
  10. 10.
    Zbilut, J.P., Webber Jr., C.L.: Embeddings and delays as derived from quantification of recurrence plots. Phys. Lett. A 171, 199–203 (1992)CrossRefGoogle Scholar
  11. 11.
    Marwan, N., Wessel, N., Meyerfeldt, U. (eds.): Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Phys. Reviw. E 66, 26702 (2002)CrossRefGoogle Scholar
  12. 12.
    Lippmann, R.P.: An Introduction to Computing with Neural nets. IEEE ASSP Magazine, 4–22 (April 1987)Google Scholar
  13. 13.
    Mirchandami, G., Cao, W.: On hidden nodes for neural nets. IEEE Trans. on Circuits and System 36, 661–664 (1989)CrossRefGoogle Scholar
  14. 14.
    Vogel, M.A., Wong, A.K.C.: PFS clustering method. IEEE Trans. on Pattern Anal. Mach. Intell. 1, 237–245 (1979)CrossRefMATHGoogle Scholar
  15. 15.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic, San Diego (1990)MATHGoogle Scholar
  16. 16.
    Drummond, J.C., Brann, C.A., Perkins, D.E.: A comparison of median frequency, spectral edge frequency, a frequency band power ratio, total power, and dominance shift in determination of depth of anaesthesia. Acta Anaesthesiologica Scand 35, 693–699 (1991)CrossRefGoogle Scholar
  17. 17.
    Rampil, I.J., Matteo, R.S.: Changes in EEG spectral edge frequency correlate with the hemodynamic response to laryngoscopy and intubation. Anesthesiology 67, 139–142 (1987)CrossRefGoogle Scholar
  18. 18.
    Sebel, P.S., Bowles, S.M., Saini, V. (eds.): EEG bispectrum predicts movement during thiopental/isoflurane anaesthesia. J. Clin. Monit. 11, 83–91 (1995)CrossRefGoogle Scholar
  19. 19.
    Linkens, D.A.: Adaptive and intelligent control in anesthesia. IEEE Contr. Syst. Technol. Dec., 6–11 (1992)Google Scholar
  20. 20.
    Argyris, J.H., Faust, G., Haase, M.: An Exploration of Chaos. North-Holland, Amsterdam (1994)MATHGoogle Scholar
  21. 21.
    Ott, E.: Chaos in Dynamical Systems. Cambridge University Press, Cambridge (1993)MATHGoogle Scholar
  22. 22.
    Quasha, A.L., Eger, E.I., Tinker, H.H.: Determination and applications of MAC. Anesthesiol. 53, 315–334 (1980)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Liyu Huang
    • 1
    • 2
  • Weirong Wang
    • 1
    • 3
  • Sekou Singare
    • 2
  1. 1.Department of Biomedical EngineeringXidian UniversityXi’anChina
  2. 2.Institute of Biomedical EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.Department of Medical InstrumentationShanhaidan HospitalXi’anChina

Personalised recommendations