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Optimal wavelets for biomedical signal compression

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Abstract

Signal compression is gaining importance in biomedical engineering due to the potential applications in telemedicine. In this work, we propose a novel scheme of signal compression based on signal-dependent wavelets. To adapt the mother wavelet to the signal for the purpose of compression, it is necessary to define (1) a family of wavelets that depend on a set of parameters and (2) a quality criterion for wavelet selection (i.e., wavelet parameter optimization). We propose the use of an unconstrained parameterization of the wavelet for wavelet optimization. A natural performance criterion for compression is the minimization of the signal distortion rate given the desired compression rate. For coding the wavelet coefficients, we adopted the embedded zerotree wavelet coding algorithm, although any coding scheme may be used with the proposed wavelet optimization. As a representative example of application, the coding/encoding scheme was applied to surface electromyographic signals recorded from ten subjects. The distortion rate strongly depended on the mother wavelet (for example, for 50% compression rate, optimal wavelet, mean±SD, 5.46±1.01%; worst wavelet 12.76±2.73%). Thus, optimization significantly improved performance with respect to previous approaches based on classic wavelets. The algorithm can be applied to any signal type since the optimal wavelet is selected on a signal-by-signal basis. Examples of application to ECG and EEG signals are also reported.

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References

  1. Burrus CS, Gopinath RA, Guo H (1997) Introduction to wavelets and wavelet transforms. Prentice-Hall, Englewood Cliffs, pp 53–66

    Google Scholar 

  2. Clarke LP, Qian W, Kallergi M, Venugopal P, Clark RA (1998) Hybrid wavelet transform for image enhancement for computer-assisted diagnosis and telemedicine applications. In: Akay M (ed) Time frequency and wavelets in biomedical signal processing. IEEE, Piscataway

    Google Scholar 

  3. Istepanian RSH, Petrosian AA (2000) Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system. IEEE Trans Inf Technol Biomed 4:200–211

    Article  Google Scholar 

  4. Jalaleddine SMS, Hutchens CG, Strattan RD, Coberly WA (1990) ECG data compression techniques: a unified approach. IEEE Trans Biomed Eng 37:329–343

    Article  Google Scholar 

  5. Jensen A, La Cour-Harbo A (2001) Ripples in mathematics. Springer, Berlin Heidelberg New York, ISBN: 3-540-41662-5

  6. Lawton W (1990) Tight frames of compactly supported affine wavelets. J Math Phys 31:1898–1901

    Article  MATH  MathSciNet  Google Scholar 

  7. Lawton W (1991) Necessary and sufficient conditions for constructing orthonormal wavelet base. J Math Phys 32:1440–1443

    Article  MATH  MathSciNet  Google Scholar 

  8. Madeleine P, Farina D, Merletti R, Arendt-Nielsen L (2002) Upper trapezius muscle mechanomyographic and electromyographic activity in humans during low force fatiguing and non-fatiguing contractions. Eur J Appl Physiol 87:327–336

    Article  Google Scholar 

  9. Maitrot A, Lucas MF, Doncarli C, Farina D (2005) Signal-dependent wavelets for electromyogram classification. Med Biol Eng Comput 43(4):487–492

    Article  Google Scholar 

  10. Mallat S (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal 11:674–693

    Article  MATH  Google Scholar 

  11. Mallat S (1999) A wavelet tour of signal processing. Academic Press, New York

    MATH  Google Scholar 

  12. Norris JA, Englehart KB, Lovely DF (2003) Myoelectric signal compression using zero-trees of wavelet coefficients. Med Eng Phys 25:739–746

    Article  Google Scholar 

  13. Proakis J (2001) Digital communications. McGraw-Hill, New York

    Google Scholar 

  14. Rajoub BA (2002) An efficient coding algorithm for the compression of ECG signals using the wavelet transform. IEEE Trans Biomed Eng 49:355–362

    Article  Google Scholar 

  15. Selesnick IW (1997) Maple and the parameterization of orthogonal wavelet bases. Available online: http://www.taco.poly.edu/selesi/theta2h/

  16. Shapiro JM (1993) Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans Signal Proc 41:3445–3462

    Article  MATH  Google Scholar 

  17. Vaidyanathan PP (1996) Multirate systems and filter banks. Wellesley–Cambridge Press, London

    Google Scholar 

  18. Wellig P, Cheng Z, Semling M, Moschytz GS (1998) Electromyogram data compression using single-tree and modified zero-tree wavelet encoding. In: Proceedings of IEEE–EMBS, pp 1303–1306

  19. Yloestalo J (1999) Data compression methods for EEG. Technol Health Care 7:285–300

    Google Scholar 

Download references

Acknowledgements

The authors are grateful to Pascal Madeleine for providing the experimental signals used for testing the compression algorithm. The study was partly supported by the project “Cybernetic Manufacturing Systems” (CyberManS), financed by the European Community.

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Authors and Affiliations

  1. Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7 D-3, 9220, Aalborg, Denmark

    Mogens Nielsen, Ernest Nlandu Kamavuako, Michael Midtgaard Andersen & Dario Farina

  2. IRCCyN, École Centrale de Nantes, Nantes, France

    Marie-Françoise Lucas

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  1. Mogens Nielsen
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  2. Ernest Nlandu Kamavuako
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  3. Michael Midtgaard Andersen
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  4. Marie-Françoise Lucas
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  5. Dario Farina
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Correspondence to Dario Farina.

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Nielsen, M., Kamavuako, E.N., Andersen, M.M. et al. Optimal wavelets for biomedical signal compression. Med Bio Eng Comput 44, 561–568 (2006). https://doi.org/10.1007/s11517-006-0062-0

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  • DOI: https://doi.org/10.1007/s11517-006-0062-0

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