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An Analytical Expression for Empirical Mode Decomposition Based on B-Spline Interpolation

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Abstract

Although empirical mode decomposition (EMD) lacks a rigorous theoretical basis, it has attracted much attention for analyzing nonstationary signals adaptively. In this paper, the EMD method is investigated from a digital signal processing perspective. Based on an analysis of extrema sampling and B-spline interpolation, we show that the upper and lower envelopes of signals are formed by a succession of three basic operations: decimation of local extrema, interpolation, and filtering by a B-spline filter. We then show that some aliasing noise can be suppressed by the mean of the envelopes, though the extrema sampling is a sub-Nyquist sampling. For uniformly spaced extrema of signals, we derive a general analytical expression of intrinsic mode functions (IMFs) extracted by the EMD method from signals.

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References

  1. B. Boashash, Estimating and interpreting the instantaneous frequency of a signal—part 1: fundamentals. Proc. IEEE 80(4), 520–538 (1992)

    Article  Google Scholar 

  2. C.D. Boor, A Practical Guide to Splines—Revised Edition (Springer, New York, 2001)

    Google Scholar 

  3. A.O. Boudraa, J.C. Cexus, EMD-based signal filtering. IEEE Trans. Instrum. Meas. 56(6), 2196–2202 (2007)

    Article  Google Scholar 

  4. A. Bouzid, N. Ellouze, Maximum error in discrete EMD decomposition of periodic signals, in Proc. 15th Int. Conf. Digital Signal Process (2007), pp. 563–566

    Google Scholar 

  5. Q.H. Chen, N. Huang, S. Riemenschneider et al., A B-spline approach for empirical mode decomposition. Adv. Comput. Math. 24, 171–195 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  6. R. Deering, J.F. Kaiser, The use of a masking signal to improve empirical mode decomposition, in Proc. IEEE Int. Conf. Acoust., Speech Signal Process (2005), pp. 485–488

    Google Scholar 

  7. E.H.S. Diop, R. Alexandre, A.O. Boudraa, A PDE characterization of the intrinsic mode function, in Proc. IEEE Int. Conf. Acoust., Speech Signal Process (2009), pp. 3429–3432

    Google Scholar 

  8. P. Flandrin, G. Rilling, P. Goncalvés, Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11(2), 112–114 (2004)

    Article  Google Scholar 

  9. M. Henker, G. Fettweis, Extended algorithms for sample rate conversion, in Proc. 2nd Karlsruhe Workshop on Software Radios (2002), pp. 33–40

    Google Scholar 

  10. N.E. Huang, Z. Shen, S.R. Long et al., The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 454, 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  11. A.J. Jerri, The Shannon sampling theorem—its various extensions and applications: a tutorial review. Proc. IEEE 65(11), 1565–1596 (1977)

    Article  MATH  Google Scholar 

  12. Y. Kopsinis, S. McLaughlin, Investigation and performance enhancement of the empirical mode decomposition method based on a heuristic search optimization approach. IEEE Trans. Signal Process. 56(1), 1–13 (2008)

    Article  MathSciNet  Google Scholar 

  13. D.S. Laila, A.R. Messina, B.C. Pal, A refined Hilbert–Huang transform with applications to interarea oscillation monitoring. IEEE Trans. Power Syst. 24(2), 610–620 (2009)

    Article  Google Scholar 

  14. J. Li, R.M. Gray, Context-based multiscale classification of document images using wavelet coefficient distributions. IEEE Trans. Image Process. 9(9), 1604–1616 (2000)

    Article  Google Scholar 

  15. N. Rehman, D.P. Mandic, Multivariate empirical mode decomposition. Proc. R. Soc. Lond. A 466, 1291–1302 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. G. Rilling, P. Flandrin, One or two frequencies? The empirical mode decomposition answers. IEEE Trans. Signal Process. 56(1), 85–95 (2008)

    Article  MathSciNet  Google Scholar 

  17. N. Senroy, S. Suryanarayanan, Two techniques to enhance empirical mode decomposition for power quality applications, in Proc. IEEE Power Eng. Society General Meeting (2007), pp. 1–6

    Google Scholar 

  18. N. Senroy, S. Suryanarayanan, P.F. Ribeiro, An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality. IEEE Trans. Power Syst. 22(4), 1843–1850 (2007)

    Article  Google Scholar 

  19. M. Unser, Splines: a perfect fit for signal and image processing. IEEE Signal Process. Mag. 16(6), 22–38 (1999)

    Article  Google Scholar 

  20. M. Unser, Sampling—50 years after Shannon. Proc. IEEE 88(4), 569–587 (2000)

    Article  Google Scholar 

  21. M. Unser, A. Aldroubi, M. Eden, Fast B-spline transforms for continuous image representation and interpolation. IEEE Trans. Pattern Anal. Mach. Intell. 13, 277–285 (1991)

    Article  Google Scholar 

  22. M. Unser, A. Aldroubi, M. Eden, Polynomial spline signal approximations: filter design and asymptotic equivalence with Shannon’s sampling theorem. IEEE Trans. Inf. Theory 38(1), 95–103 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  23. M. Unser, A. Aldroubi, M. Eden, B-spline signal processing: part I—theory. IEEE Trans. Signal Process. 41(2), 821–833 (1993)

    Article  MATH  Google Scholar 

  24. P.P. Vaidyanathan, Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial. Proc. IEEE 78(1), 56–93 (1990)

    Article  MathSciNet  Google Scholar 

  25. Z.H. Wu, N.E. Huang, A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. A 460, 1597–1611 (2004)

    Article  MATH  Google Scholar 

  26. Z.H. Wu, N.E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1, 1–41 (2009)

    Article  Google Scholar 

  27. Y.L. Yang, Theoretical analysis and application investigation of empirical mode decomposition. Ph.D. dissertation, Sch. of Mech. Eng., Beijing Inst. of Technol., Beijing, China, 2010

  28. Y.L. Yang, J.H. Deng, W.C. Tang et al., Nonuniform extrema resampling and empirical mode decomposition. Chin. J. Electron. 18(4), 759–762 (2009)

    Google Scholar 

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Acknowledgement

The authors would like to thank Prof. Ran Tao for many helpful discussions.

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Correspondence to Yanli Yang.

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Yang, Y., Miao, C. & Deng, J. An Analytical Expression for Empirical Mode Decomposition Based on B-Spline Interpolation. Circuits Syst Signal Process 32, 2899–2914 (2013). https://doi.org/10.1007/s00034-013-9592-5

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