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Wavelets pp 132–138Cite as

Wavelet Transformations in Signal Detection

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Part of the book series: Inverse Problems and Theoretical Imaging ((IPTI))

Abstract

A new method for dealing with transient signals has recently appeared in the literature [2–11]. The basis functions are referred to as wavelets, and they employ time compression (or dilation) rather than a variation of frequency of the modulated sinusoid. Hence all the wavelets have the same number of cycles. The analyzing wavelets must satisfy a few simple conditions, but are not otherwise specified. There is therefore a wide latitude in the choice of these functions and they can be taylored to specific applications. We have applied them to detect ventricular delayed potentials (VLP) in the electrocardiogram.

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References

  1. D. Gabor, “Theory of Communications,” J. IEE. Vol. 93(3), (1946), pp 429–457.

    Google Scholar 

  2. A. Grossman and J. Morlet, “Decomposition of Functions into Wavelets of Constant Shape, and Related Transforms.” Center for Interdisciplinary Research and Research Center Bielefeld-Bochum-Stochastics, University of Bielefeld, Report No. 11, December 1984. Also in “Mathematics and Physics 2,” L. Streit, editor. World Scientific Publishing Co., Singapore.

    Google Scholar 

  3. P. Goupillaud, A. Grossman, and J. Morlet, “Cycle-Octave and Related Transforms in Seismic Signal Analysis,” Geoexploration 23, 85 (1984/85)

    Article  Google Scholar 

  4. A. Grossman, J. Morlet, T. Paul, “Transforms associated to square-integrable group representations, I: General Results. J. Math. Phys 26 2473, (1985).

    Article  MathSciNet  ADS  Google Scholar 

  5. A. Grossman, J. Morlet, T. Paul, “Transforms associated to square-integrabie group representations, II: Examples. Ann. Inst. H. Poincaré 45 293 (1986).

    Google Scholar 

  6. I. Daubechies, A. Grossman, Y. Meyer, “Painless nonorthogona1 expansions”, J.Math. Phys. 27 pl271, (1986).

    Article  Google Scholar 

  7. A. Grossman and J. Morlet, “Decomposition of Hardy functions into square integrable wavelets of constant shape”, SIAM J. Math. Anal. 15., p.723, (1984).

    Article  MathSciNet  Google Scholar 

  8. R. Kronland-Martinet, J. Morlet, A. Grossman “Analysis of Sound Patterns through Wavelet Transforms.” To appear in International Journal of Pattern Recognition and Artificial Intelligence, Special Issue on Expert Systems and Pattern Analysis.

    Google Scholar 

  9. S. Mallat, “A Theory for Multi seale Decomposition: The Scale Change Representation,” Grasp Laboratory, Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104–6389 (1986).

    Google Scholar 

  10. R. R. Coifman and Y. Meyer, “The discrete Wavelet Transform”, Preprint, Yale University Department of Mathematics, 1987.

    Google Scholar 

  11. Y. Meyer, S. Jaffard, O. Rioul, “L’analyse par ondelettes,” Pour la Science, Sept. 1987, pp 28–37.

    Google Scholar 

  12. D. Marr, Vision, W. H Freeman and Company, 1982.

    Google Scholar 

  13. Papoulis. The Fourier Integral and its Applications McGraw-Hill Book Co. Inc., 1962, p64.

    Google Scholar 

  14. E. J. Berbari, B. J. Scherlag, R. R. Hope, R. Lazzara,” Recording from the body surface of arrhythmogenic ventricular activity during the S_T segment”. Am. J. Cardiol. 41(4), p697–702, (April, 1978).

    Article  Google Scholar 

  15. G. Breithardt and M. Borggrefe, “Pathophysiological mechanisms and clinical significance of ventricular late potentials”. Eur. Heart J. 7(5), pp 364–85, (May, 1986).

    Google Scholar 

  16. D. L. Kuchar, C. W. Thoburn, N. L. Sammel, “Prediction of Serious Arrhythmic Events after Myocardial Infarction: Signal-Averaged Electrocardiogram, Holter Monitoring and Radionuclide Ventriculography,” JACC vol 9., No.3, March 1987, pp 531–8, (Australia).

    Google Scholar 

  17. E. S. Gang, T. Peter, M. E. Rosenthal, W. J. Mandel, and Y. Lass, “Detection of Late Potentials on the Surface Electrocardiogram in Unexplained Syncope,” Am. J. Cardiol, vol. 58, Nov. 1986, pp 1014–1020.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Yale University, 06520, New Haven, CT, USA

    F. B. Tuteur

Authors
  1. F. B. Tuteur
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Editor information

Editors and Affiliations

  1. Centre National de la Recherche Scientifique, Luminy - Case 907, F-13288, Marseille Cedex 9, France

    Jean-Michel Combes , Alexander Grossmann  & Philippe Tchamitchian ,  & 

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© 1989 Springer-Verlag Berlin Heidelberg

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Tuteur, F.B. (1989). Wavelet Transformations in Signal Detection. In: Combes, JM., Grossmann, A., Tchamitchian, P. (eds) Wavelets. Inverse Problems and Theoretical Imaging. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-97177-8_8

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  • DOI: https://doi.org/10.1007/978-3-642-97177-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-97179-2

  • Online ISBN: 978-3-642-97177-8

  • eBook Packages: Springer Book Archive

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