Advertisement

Radiophysics and Quantum Electronics

, Volume 61, Issue 8–9, pp 689–703 | Cite as

Finding Morphology Points of Electrocardiographic-Signal Waves Using Wavelet Analysis

  • A. I. Kalyakulina
  • I. I. Yusipov
  • V. A. Moskalenko
  • A. V. Nikolskiy
  • A. A. Kozlov
  • N. Yu. Zolotykh
  • M. V. IvanchenkoEmail author
Article

We propose a new algorithm for determining the basic significant points of various electrocardiographic-signal waves taking into account information from all available leads and ensuring a similar or higher accuracy compared with that of other up-to-date technologies. The test results of the algorithm efficiency for the QT data base [1] show a sensitivity above 97% when detecting the electrocardiographic-signal peaks, and 96% for their onsets and offsets, as well as the positive predictive value exceeding 97% for the peaks of the complexes, which is the best result compared with those of the previously known algorithms. As distinct from them, the proposed approach also allows one to determine the wave morphology. For the proposed algorithm, the delineation errors of all significant points are below the tolerances specified by the Committee of General Standards for Electrocardiography.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    A. Grossmann and J. Morlet, SIAM J. Math. Anal., 15, No. 4, 723 (1984).MathSciNetCrossRefGoogle Scholar
  3. 3.
    Y. Meyer, Wavelets: Algorithms and Applications, Society for Industrial and Applied Mathematics, Philadelphia (1993).Google Scholar
  4. 4.
    N. M. Astaf’eva, Physics –Uspekhi, 39, No. 11, 1085 (1996).ADSCrossRefGoogle Scholar
  5. 5.
    I. M. Dremin, O.V. Ivanov, and V. A. Nechitailo, Physics –Uspekhi, 44, No. 5, 447 (2001).ADSCrossRefGoogle Scholar
  6. 6.
    E. B. Postnikov, Phys. Rev. E, 80, 057201 (2009).ADSCrossRefGoogle Scholar
  7. 7.
    A. A. Anisimov, O. N. Pavlova, A. N. Tupitsyn, and A. N. Pavlov, Izv. Vyssh. Uchebn. Zaved., Prikl. Nelin. Din., 16, No. 11, 3 (2008).Google Scholar
  8. 8.
    A. A. Koronovskii, V. A. Makarov, A. N. Pavlov, et al., Wavelets in Neurodynamics and Neurophysiology [in Russian], Fizmatlit, Moscow (2013).Google Scholar
  9. 9.
    I. V. Dyachenko and N. I. Chervyakov, Infokommun. Tekhnol., 3, No. 4, 6 (2005).Google Scholar
  10. 10.
    G. M. Teptin, I. A. Latfullin, L. E. Mamedova, and Z. F. Kim, Radiophys. Quantum Electron., 54, No. 3, 210 (2011).ADSCrossRefGoogle Scholar
  11. 11.
    G. S. Hooper, P. Yellowlees, T. H. Marwick, et al., J. Telemed. Telecare, 7, 249 (2001).CrossRefGoogle Scholar
  12. 12.
    J. A. A. Fairweather, P. Johnston, S. Luo, and P. W. Macfarlane, Comp. Cardiol., 34, No. 5, 193 (2007).Google Scholar
  13. 13.
    S. Lee, I. Y. Kim, and Y. C. Park, Comp. Meth. Prog. Biomed., 87, No. 3, 254 (2007).CrossRefGoogle Scholar
  14. 14.
    D. N. Ivlev and I. Ya. Orlov, Radiophys. Quantum Electron., 47, No. 7, 535 (2004).ADSCrossRefGoogle Scholar
  15. 15.
    M. G. Khan, Fast ECG Analysis [in Russian], Binom, Moscow (2009).Google Scholar
  16. 16.
    J. Martinez, R. Almeida, S. Olmos, et al., IEEE Trans. Biomed. Eng., 51, No. 4, 570 (2004).CrossRefGoogle Scholar
  17. 17.
    P. Addison, Physiolog. Meas., 26, No. 3, 155 (2005).ADSCrossRefGoogle Scholar
  18. 18.
    L. Y. Di Marco and L. Chiari, BioMed. Eng. OnLine, 10, 23 (2011).CrossRefGoogle Scholar
  19. 19.
    J. M. Bote, J. Recas, F. Rincón, et al., IEEE J. Biomed. Health Inform., 22, No. 2, 429 (2017).CrossRefGoogle Scholar
  20. 20.
    C. Li, C. Zheng, and C. Tai, IEEE Trans. Biomed. Eng., 42, No. 1, 21 (1995).ADSCrossRefGoogle Scholar
  21. 21.
    S. Mallat, IEEE Trans. Acoust. Speech Sign. Proc., 37, No. 12, 2091 (1989).CrossRefGoogle Scholar
  22. 22.
    A. Cohen and J. Kovacevic, Proc. IEEE, 84, No. 4, 514 (1996).CrossRefGoogle Scholar
  23. 23.
    I. A. Latfullin, Z. F. Kim, and G. M. Teptin, Vest. Aritm., No. 53, 44 (2009).Google Scholar
  24. 24.
    S. G. Mallat and S. Zhong, IEEE Trans. Pat. Anal. Mach. Intel., 14, No. 7, 710 (1992).CrossRefGoogle Scholar
  25. 25.
    P. Laguna, R. G. Mark, A. L. Goldberger, and G. B. Moody, Comput. Cardiol., 24, 673 (1997).Google Scholar
  26. 26.
    A. L. Goldberger, L. A. N. Amaral, L. Glass, et al., Circulation, 101, No. 23, e215 (2000).CrossRefGoogle Scholar
  27. 27.
    Association for the Advancement of Medical Instrumentation, NSI/AAMI EC57:1998/(R)2008 (Revision of AAMI ECAR: 1987) (1999).Google Scholar
  28. 28.
    F. Rincón, J. Recas, N. Khaled, and D. Atienza, IEEE Trans. Inf. Technol. Biomed., 15, No. 6, 854 (2011).CrossRefGoogle Scholar
  29. 29.
    “The CSE working party,” Eur. Heart J., 6, No. 10, 815 (1985).Google Scholar
  30. 30.
    S. Mehta and N. Lingayat, in: Proc. World Congress on Engineering and Computer Sciences, San Francisco, October 22–24, 2008, p. 22.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • A. I. Kalyakulina
    • 1
  • I. I. Yusipov
    • 1
  • V. A. Moskalenko
    • 1
  • A. V. Nikolskiy
    • 1
    • 2
  • A. A. Kozlov
    • 3
  • N. Yu. Zolotykh
    • 1
  • M. V. Ivanchenko
    • 1
    Email author
  1. 1.N. I. Lobachevsky State University of Nizhny NovgorodNizhny NovgorodRussia
  2. 2.City Clinical Hospital No. 5Nizhny NovgorodRussia
  3. 3.N. A. Semashko Regional Clinical Hospital of Nizhny NovgorodNizhny NovgorodRussia

Personalised recommendations