Advertisement

Biophysics

, Volume 63, Issue 5, pp 779–790 | Cite as

A Triangulation Approach to Checking the Reliability of Estimation of Oscillatory Modes Embedded in an ECG Signal

  • A. G. LastovetskyEmail author
  • E. N. MininaEmail author
COMPLEX SYSTEMS BIOPHYSICS

Abstract—It is important to perform an analytical study of the oscillatory modes embedded in an ECG signal on individuals in certain occupations (power station operators, pilots, military officers, drivers, sportspeople, etc.) and those who experience pronounced emotional stress while carrying out important work. Reliability of studying the oscillatory modes was achieved by using and comparing several methods (triangulation), thus allowing evaluation of the results obtained by studying spatio-temporal variances, the ordered vs. stochastic character, and the periodic vs. stochastic character of the dynamics of an ECG signal. The oscillatory modes of the ECG signal structure were studied in various conditions using graphic illustrations obtained by converting the ECG signal in the phase plane. The orderliness, periodicity, and stochastic features of a time series of R–R intervals were examined using the entropy-dynamic approach and the phase-plane and phase-curve methodologies. Nonlinear phenomena were qualitatively described using the elementary part of the theory of catastrophes. Dysfunctional and pathological conditions were associated with either a significant expansion of the phase graph (PG) shape with significant variations in amplitude and time parameters and an increase in the degree of chaos or a distinct periodicity with a loss of variability, a decrease in the chaotic component, and a mathematical degeneration of the cycle. The results of the ECG-based entropy analysis and its PG were quantitatively comparable with data obtained by a rheographic recording of the biological signal; this finding supporting the reliability of the results. Constructing a phase curve using a nonparametric method helped us to detect hidden functional features of the cardiodynamics system. This study substantially contributes to the development of preventive methods based on testing cardiac activity in the primary medical and social care system.

Keywords: triangulation ECG signal oscillatory mode 

Notes

REFERENCES

  1. 1.
    L. Glass, Nature 410, 277 (2001).ADSCrossRefGoogle Scholar
  2. 2.
    L. V. Mezentseva, Biophysics (Moscow) 56 (3), 510 (2011).CrossRefGoogle Scholar
  3. 3.
    L. V. Mezentseva, Biophysics (Moscow) 59 (1), 119 (2014).CrossRefGoogle Scholar
  4. 4.
    O. L. Bokeriya and M. B. Biniashvili, Ann. Aritmol. 10 (2), 79 (2013).CrossRefGoogle Scholar
  5. 5.
    P. S. Chan, J. Amer. Coll. Cardiol. 48, 112 (2006).MathSciNetCrossRefGoogle Scholar
  6. 6.
    N. V. Arteyeva and J. E. Azarov, Ann. Noninvasive Electrocardiol. 22 (1), 12360 (2017).CrossRefGoogle Scholar
  7. 7.
    L. V. Mezentseva, S. S. Pertsov, F. Yu. Kopilov, and A. G. Lastovetsky, Biophysics (Moscow) 62 (3), 499 (2017).CrossRefGoogle Scholar
  8. 8.
    O. A. Voloshina, V. P. Oleinik, S. N. Kulish, and Al Otti Sami, Radioelectr. Komp’yut. Sist., No. 4 (45), 29 (2010).Google Scholar
  9. 9.
    E. N. Minina and L. S. Fainzilberg, Vestn. Nov. Med. Tekhnol. 21 (3), 22 (2014).Google Scholar
  10. 10.
    E. N. Minina, Vestn. Nov. Med. Tekhnol. 8 (1), 1-8 (2014).Google Scholar
  11. 11.
    E. N. Minina, Khimiya 1 (67) (4), 26 (2015)Google Scholar
  12. 12.
    E. N. Minina, Uch. Zap. Tavrich. Nats. Univ. im. V. I. Vernadskogo, Ser. Biol. Khim. 26(65) (2), 148 (2013)Google Scholar
  13. 13.
    L. S. Fainzilberg and E. N. Minina, Klin. Inform. Telemed. 9 (10), 33 (2013)Google Scholar
  14. 14.
    L. S. Fainzilberg and E. N. Minina, Kibernet. Vychisl. Tekh., No. 1, 5 (2014)Google Scholar
  15. 15.
    E. N. Minina and L. S. Fainzilberg, Ross. Kardiol. Zh. 12 (128), 7 (2015).Google Scholar
  16. 16.
    A. A. Yashin, Living Matter (LKI, Moscow, 2010) [in Russian].Google Scholar
  17. 17.
    Yu. L. Klimontovich, Usp. Fiz. Nauk 158 (1), 59 (1989).CrossRefGoogle Scholar
  18. 18.
    V. I. Shapovalov and N. V. Kazakov, Nat. Sci. 6 (7), 467 (2014).Google Scholar
  19. 19.
    V. I. Shapovalov, Int. J. Appl. Math. Stat. 26 (16), (2012).Google Scholar
  20. 20.
    E. N. Minina and A. G. Lastovetskii, Vestn. Nov. Med. Tekhnol. 12 (2), 2−1, (2018).Google Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  1. 1.Central Research Institute of Health Care Organization and Informatization, Ministry of Health of the Russian FederationMoscowRussia
  2. 2.aurid Academy, Vernadsky Crimean Federal UniversitySimferopolTRussia

Personalised recommendations