Skip to main content
Springer Nature Link
Log in

Analysis of Early Hypoxia EEG Based on a Novel Chaotic Neural Network

  • Conference paper
Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4232))

Included in the following conference series:

Abstract

This paper presents an experiment to recognize early hypoxia based on EEG analyses. A chaotic neural network, the KIII model, initially designed to model olfactory neural systems is utilized for pattern classification. The experimental results show that the EEG pattern can be detected remarkably at an early stage of hypoxia for individuals.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Saroj, K.L.L., Ashley, C.: A critical review of the psychophysiology of driver fatigue. Biological Psychology 55, 173–194 (2001)

    Article  Google Scholar 

  2. Vuckovic, A., Andrew, R.V., Chen, C.N., Popovic, D.: Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Medical Engineering & Physics 24, 349–360 (2002)

    Article  Google Scholar 

  3. Zhang, X.S., Rob, J.: EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering 48, 12 (2001)

    Article  Google Scholar 

  4. Kozma, R., Freeman, W.J.: Chaotic Resonance - Methods and Applications for Robust Classification of Noisy and Variable Patterns. Int. J. Bifurcation and Chaos. 11(6), 1607–1629 (2001)

    Article  Google Scholar 

  5. Li, G., Lou, Z., Wang, L., Li, X., Freeman, W.J.: Application of Chaotic Neural Model Based on Olfactory System on Pattern Recognitions. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 378–381. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Quarder, S., Claussnitzer, U., Otto, M.: Using Singular-Value Decompositions to Classify Spatial Patterns Generated by a Nonlinear Dynamic Model of the Olfactory System. Chemometrics and Intelligent Laboratory Systems 59, 45–51 (2001)

    Article  Google Scholar 

  7. Hu, M., Li, J.J., Li, G., Tang, X.W., Freeman, W.J.: Normal and Hypoxia EEG Recog-nition Based on a Chaotic Olfactory Model. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 554–559. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Chang, H.J., Freeman, W.J.: Optimization of Olfactory Model in Software to Give 1/f Power Spectra Reveals Numerical Instabilities in Solutions Governed by Aperiodic (Chaotic) Attractors. Neural Networks 11, 449–466 (1998)

    Article  Google Scholar 

  9. Chang, H.J., Freeman, W.J., Burke, B.C.: Biologically Modeled Noise Stabilizing Neuro-dynamics for Pattern Recognition. Int. J. Bifurcation and Chaos. 8(2), 321–345 (1998)

    Article  MATH  Google Scholar 

  10. Freeman, W.J., Chang, H.J., Burke, B.C., Rose, P.A., Badler, J.: Taming Chaos: Stabilization of Aperiodic Attractors by Noise. IEEE Transactions on Circuits and Systems 44, 989–996 (1997)

    Article  MathSciNet  Google Scholar 

  11. Principe, J.C., Tavares, V.G., Harris, J.G., Freeman, W.J.: Design and Implementation of a Biologically Realistic Olfactory Cortex in Analog VLSI. Proceedings of the IEEE 89(7), 1030–1051 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Physics, Zhejiang University, Hangzhou, 310027, China

    Meng Hu

  2. Hangzhou Sanitarium of PLA Airforce, Hangzhou, 310013, China

    Jiaojie Li

  3. National Laboratory of Industrial Control Technology, Institute of Advanced, Process Control, Zhejiang University, Hangzhou, 310027, China

    Guang Li

  4. Division of Neurobiology, University of California at Berkeley, LSA 142, Berkeley, CA, 94720-3200, USA

    Walter J. Freeman

Authors
  1. Meng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiaojie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Walter J. Freeman
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hu, M., Li, J., Li, G., Freeman, W.J. (2006). Analysis of Early Hypoxia EEG Based on a Novel Chaotic Neural Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_2

Download citation

  • DOI: https://doi.org/10.1007/11893028_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46479-2

  • Online ISBN: 978-3-540-46480-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics