Advertisement

The Computation of Atrial Fibrillation Chaos Characteristics Based on Wavelet Analysis

  • Jianrong Hou
  • Hui Zhao
  • Dan Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4681)

Abstract

Atrial fibrillation data series show the non-linear and chaos characters in the process of time-space kinetics evolution. In the case of unknowing the fractal dimension of atrial fibrillation chaos, the process of querying the similarity of diagnosis curve figure will be affected to a certain degree. An evaluation formula of varying-time Hurst index is established by wavelet and the algorithm of varying-time index is presented, which is applied to extract the characteristics of the atrial fibrillation in this paper. The diagnosis of atrial fibrillation curve figure can be done at some resolution ratio level. The results show that the time-varying fractal dimension rises when atrial fibrillation begins, while it falls when atrial fibrillation ends. The begin and the end characteristics of atrial fibrillation can be successfully detected by means of the change of the time-varying fractal dimension. The results also indicate that the complexity of heart rate variability (HRV) decreases at the beginning of atrial fibrillation. The effectiveness of the method is validated by means of the HRV example in the end.

Keywords

Atrial fibrillation Wavelet analysis Chaos 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Goldberger, A. l., Rigney, D.R., Mietus, J., Antman, E.M., Greenwald, S.: Experientia 44, 11-12 (1988)Google Scholar
  2. 2.
    Peng, C.K., Havlin, S., Stanley, H.E., Goldberger, A.L.: Chaos 5, 82-92 (1995)Google Scholar
  3. 3.
    Ruan, J., Cai, Z.J., Lin, W.: Proc. IEEE-EMBS Asia-Pacific on Biomed. Egin., 363–368 (2000)Google Scholar
  4. 4.
    Peng, C.K., Mietus, J., Hausdorff, J.M., Havlin, S., Stanley, H.E., Goldberger, A.L.: Phys. Rev. Lett. 70, 1343–1352 (1993)Google Scholar
  5. 5.
    Wornell, G.W.: Signal Processing with Fractals: a Wavelet-Based Approach. Prentice Hall, Englewood Cliffs (1996)Google Scholar
  6. 6.
    Kobayashi, M., Musha, T.: IEEE Trans. Biomed. Eng. 29, 456–462 (1982)CrossRefGoogle Scholar
  7. 7.
    Hou, J.R., Song, G.X.: Application of Wavelet Analysis in the Estimation of Hurst Index. Journal of Xidian University(Science Edition) 1, 121–125 (2002)Google Scholar
  8. 8.
    Inrid, D.: The wavelet transform: Time-Frequency Localization and Signal Analysis IEEE. Trans. On Information Theory 36 (1990)Google Scholar
  9. 9.
    Brockwell, P.J.: Time series: Theory and Methods. Springer-Verlag, New York (1991)Google Scholar
  10. 10.
    Mandebrot, B.B., Van, J.W.: Ness, Fractional Brownian motions, fractional noises and applications. SIAM Review 10(4), 422–437 (1968)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Hou, j.r., Zhao, H., Shi, B.L.: A New Method for Similarity Matching of Non-Stationary Times Series Based on Fractal Time-Varying Dimension. Chinese Journal of Computers 2, 227–231 (2005)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jianrong Hou
    • 1
  • Hui Zhao
    • 2
  • Dan Huang
    • 1
  1. 1.School of Management, Shanghai Jiaotong University, Shanghai, 200052China
  2. 2.Software Engineering Institute, East China Normal University, Shanghai, 200062China

Personalised recommendations