Advertisement

Wavelet-Based Multiscale Sample Entropy and Chaotic Features for Congestive Heart Failure Recognition Using Heart Rate Variability

  • Sung-Nien YuEmail author
  • Ming-Yuan Lee
Original Article

Abstract

This study explores the discrimination power of a multiscale analysis method based on the discrete wavelet transform (DWT) in characterizing nonlinear features for congestive heart failure (CHF) recognition. Two DWT paradigms, namely standard DWT and DWT with reconstruction (RDWT), were employed to characterize two categories of nonlinear features, namely sample entropy (SE) and chaotic features, for CHF recognition based on heart rate variability (HRV). The performance of the wavelet-based analysis methods was compared to that of a traditional coarse grained average (CGA) method. The support vector machine was used as a classifier and the capability of the features was evaluated using the leave-one-out cross-validation method. The results show that when using solely SE features, all three multiscale analysis methods (CGA, DWT, and RDWT) with five dyadic scales outperform traditional CGA with twenty consecutive scales in characterizing HRV for CHF recognition. When using chaotic features calculated from the five dyadic scales, RDWT outperformed DWT and CGA with sensitivity, specificity, and accuracy rates of 95.45, 97.22, and 96.55 %, respectively. This performance was even superior to that obtained using both SE and chaotic features. The proposed multiscale analysis method using 5-scale RDWT and chaotic features outperforms three well-known CHF classifiers reported in the literature.

Keywords

Multiscale Discrete wavelet transform Sample entropy Chaotic features Congestive heart failure Heart rate variability 

Notes

Acknowledgments

This study was supported in part by Grants NSC 100-2221-E-194-063 and NSC 101-2221-E-194-019 from the National Science Council, Taiwan.

References

  1. 1.
    Malik, M. (1996). Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Circulation, 93, 1043–1065.CrossRefGoogle Scholar
  2. 2.
    Asyali, M. H. (2003) Discrimination power of long-term heart rate variability measures. In Proceedings of the 25th Annual International Conference of the IEEE EMBS (pp. 200–203). Cancum, Mexico.Google Scholar
  3. 3.
    Marwan, N., Wessel, N., Meyerfwld, U., Schirdewan, A., & Kurths, J. (2002). Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Physical Review E, 66, 026702.CrossRefGoogle Scholar
  4. 4.
    Sakki, M., Kalda, J., Vainu, M., & Laan, M. (2004). The distribution of low-variability periods in human heartbeat dynamics. Physica A, 338, 255–260.CrossRefGoogle Scholar
  5. 5.
    Skinner, J. E., Goldberger, A. L., Mayer-Kress, G., & Ideker, R. E. (1990). Chaos in the heart: Implications for clinical cardiology. Nature Biotechnology, 8, 1018–1024.CrossRefGoogle Scholar
  6. 6.
    Poon, C.-S., & Merrill, C. K. (1997). Decrease of cardiac chaos in congestive heart failure. Nature, 389, 492–495.CrossRefGoogle Scholar
  7. 7.
    Kantz, H., & Schreiber, T. (2004). Nonlinear time series analysis (2nd ed.). Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  8. 8.
    Costa, M., Goldberger, A. L., & Peng, C.-K. (2002). Multiscale entropy analysis of complex physiological time series. Physical Review Letters, 89, 068102.CrossRefGoogle Scholar
  9. 9.
    Thuraisingham, R. A., & Gottwald, G. A. (2006). On multiscale entropy analysis for physiological data. Physica A, 366, 323–332.CrossRefGoogle Scholar
  10. 10.
    Mallat, S. (2009). A wavelet tour of signal processing: The sparse way (3rd ed.). Burlington: Elsevier Inc.Google Scholar
  11. 11.
    Lee, M.-Y. and Yu, S.-N. (2012) Multiscale sample entropy based on discrete wavelet transform for clinical heart rate variability recognition. In Proceedings of the 34th Annual International Conference of the IEEE EMBS, San Diego, CA, USA (pp. 4299–4302).Google Scholar
  12. 12.
    Richmann, J. S., & Moorman, J. R. (2000). Physiological time series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278, H2039–H2049.Google Scholar
  13. 13.
    Isler, Y., & Kuntalp, M. (2007). Combining classical HRV indices with wavelet entropy measures to performance in diagnosing congestive heart failure. Computers in Biology and Medicine, 37, 1502–1510.CrossRefGoogle Scholar
  14. 14.
    Melillo, P., Fusco, R., Sansone, M., Bracale, M., & Pecchia, L. (2011). Discrimination power of long-term heart rate variability measures for chronic heart failure detection. Medical & Biological Engineering & Computing, 49, 67–74.CrossRefGoogle Scholar
  15. 15.
    Kheder, G., Taleb, R., Kachouri, A., Massoued, M. B. and Samet, M. (2009) Feature extraction by wavelet transforms to analyze the heart rate variability during two meditation technique, Chapter 32. In Advances in Numerical Methods, Lecture Notes in Electrical Engineering, New York: Springer.Google Scholar
  16. 16.
    McClellan, J. H., Schafer, R. W., & Yoder, M. A. (2003). Signal processing first, Upper Saddle River. New Jersey: Pearson Prentice Hall.Google Scholar
  17. 17.
    Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences USA, 88, 2297–2301.MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Pincus, S. M. (1995). Approximate entropy (ApEn) as a complexity measure. Chaos, 5, 110–117.CrossRefGoogle Scholar
  19. 19.
    Pincus, S. M. (2001). Assessing serial irregularity and its implications for health. Annals of the New York Academy of Sciences, 954, 245–267.CrossRefGoogle Scholar
  20. 20.
    Goldberger, A. L., & West, B. J. (1987). Applications of nonlinear dynamics to clinical cardiology. Annals of the New York Academy of Sciences, 504, 195–213.CrossRefGoogle Scholar
  21. 21.
    Takens, F. (1981). Detecting strange attractors in turbulence in dynamical system and turbulence. Berlin: Springer.Google Scholar
  22. 22.
    Grassberger, P., & Procaccia, I. (1983). Characterization of strange attractors. Physical Review Letters, 50, 346–349.MathSciNetCrossRefGoogle Scholar
  23. 23.
    Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65, 117–134.MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Albano, A. M., Muench, J., & Schwartz, C. (1988). Singular-value decomposition and Grassberger-Procaccia algorithm. Physical Review A, 38, 3017–3026.MathSciNetCrossRefGoogle Scholar
  25. 25.
    Fraser, A. M., & Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A, 33, 1134–1140.MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Kugiumtzis, D. (1996). State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length. Physica D: Nonlinear Phenomena, 95, 13–28.CrossRefzbMATHGoogle Scholar
  27. 27.
    Kim, H. S., Eykholt, R., & Salas, J. D. (1999). Nonlinear dynamics, delay times, and embedding windows. Physica D: Nonlinear Phenomena, 127, 48–60.CrossRefzbMATHGoogle Scholar
  28. 28.
    Otani, M. and Jones,A. (2000) Automated embedding and the creep phenomenon in chaotic time series. http://users.cs.cf.ac.uk/Antonia.J.Jones/UnpublishedPapers/Creep.pdf.
  29. 29.
    Cao, L. (1997). Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 110, 43–50.CrossRefzbMATHGoogle Scholar
  30. 30.
    Wolf, A., Swift, J., Swinney, H., & Vastano, J. (1985). Determining Lyapunov exponents from a time series. Physica D: Nonlinear Phenomena, 16, 285–317.MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
  32. 32.
    Yeh, R.-G., Chen, G.-Y., Shieh, J.-S., & Kuo, C.-D. (2009). Parameter investigation of detrended fluctuation analysis for short-term human heart rate variability. Journal of Medical and Biological Engineering, 30, 277–282.CrossRefGoogle Scholar
  33. 33.
    Lee, M.-Y. and Yu,S.-N. (2010) Improving discriminality in heart rate variability analysis using simple artifact and trend removal preprocessors. In Proceedings of the 32nd Annual International Conference of the IEEE EMBS (pp. 4574–4577). Buenos Aires, Argentina.Google Scholar
  34. 34.
    Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.CrossRefzbMATHGoogle Scholar
  35. 35.
    Chou, C.-M. (2011). Wavelet-based multi-scale entropy analysis of complex rainfall time series. Entropy, 13, 241–253.CrossRefGoogle Scholar
  36. 36.
    Li, P., Liu, C. Y., Wang, X. P., Li, L. P., Yang, L., Chen, Y. C., & Liu, C. C. (2013). Testing pattern synchronization in coupled systems through different entropy-based measures. Medical and Biological Engineering and Computing, 51, 581–591.CrossRefGoogle Scholar
  37. 37.
    Kaffashi, F., Foglyano, R., Wilson, C. G., & Loparo, K. A. (2008). The effect of time delay on approximate & sample entropy calculations. Physica D: Nonlinear Phenomena, 237, 3069–3074.MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Alcaraz, R., Abásolo, D., Hornero, R., & Rieta, J. J. (2010). Optimal parameters study for sample entropy-based atrial fibrillation organization analysis. Computer Methods and Programs in Biomedicine, 99, 124–132.CrossRefGoogle Scholar
  39. 39.
    Amoud, H., Snoussi, H., Hewson, D., Doussot, M., & Duchene, J. (2007). Intrinsic mode entropy for nonlinear discriminant analysis. IEEE Signal Processing Letters, 14, 297–300.CrossRefGoogle Scholar
  40. 40.
    Hu, M., & Liang, H. (2012). Adaptive multiscale entropy analysis of multivariate neural data. IEEE Transactions on Biomedical Engineering, 59, 12–15.CrossRefGoogle Scholar
  41. 41.
    Chou, Y., Zhang, A., Wang, P. & Gu, J. (2014). Pulse rate variability estimation method based on sliding window iterative DFT and Hilbert transform. Journal of Medical and Biological Engineering, 34, 347–355.Google Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2015

Authors and Affiliations

  1. 1.Department of Electrical EngineeringNational Chung Cheng UniversityMing-HsiungTaiwan

Personalised recommendations