Skip to main content
Log in

New Approaches to the Analysis and Interpretation of the Shape of Cyclic Signals

  • Published:
Cybernetics and Systems Analysis Aims and scope

Abstract

New methods for retrieving localized diagnostic information from cyclic signals of complex shape are proposed. The advantages of an alternative method of estimating the shape of an averaged cycle based on transition from a scalar signal to its mapping on the phase plane are shown. Original methods are proposed for estimating the dynamics of parameters characterizing the shape of informative fragments of the signal, based on construction of the convex hull of the phase portrait of permutation entropy and the Levenshtein distance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. A. D. Nembhard, J. K. Sinha, A. J. Pinkerton, and K. Elbhbah, “Fault diagnosis of rotating machines using vibration and bearing temperature measurements,” Diagnostyka, Vol. 14, No. 3, 45–51 (2013).

    Google Scholar 

  2. D. W. Frank, J. A. Evans, and M. R. Gorman, “Time-dependent effects of dim light at night on re-entrainment and masking of hamster activity rhythms,” J. of Biological Rhythms, Vol. 25, No. 2, 103–112 (2010). https://doi.org/10.1177/0748730409360890.

    Article  Google Scholar 

  3. R. Benkemoune, “Charles Dunoyer and the emergence of the idea of an economic cycle,” History of Political Economy, Vol. 41, No. 2, 271–295 (2009). https://doi.org/10.1215/00182702-2009-003.

    Article  Google Scholar 

  4. C. Wu, Thermodynamic Cycles: Computer-Aided Design and Optimization, Marcel Dekker, New York (2003).

    Book  Google Scholar 

  5. L. S. Fainzilberg, Information Technologies of Processing Complex Signals. Theory and Practice [in Russian], Naukova Dumka, Kyiv (2008).

    Google Scholar 

  6. S. A. Lupenko, “Deterministic and random cyclic functions as models of oscillatory phenomena and signals: Definition and classification,” Elektronnoe Modelirovanie, Vol. 28, No. 4, 29–45 (2006).

    Google Scholar 

  7. Ya. P. Dragan, “Mathematical and algorithmic software for computer-aided tools for statisticsl processing of stochastic oscillations (rhythmic processes),” Visnyk Nac. Univer. “L’vivs’ka Politekhnika:” Inform. Systemy ta Merezhi, Issue 621, No. 2, 124–130 (2008).

  8. V. N. Zvarich and B. G. Marchenko, “Linear autoregressive processes with periodic structures as models of information signals,” Radioelectronics and Communications Systems, Vol. 54, No. 7, 367–372 (2011).

    Article  Google Scholar 

  9. A. D. Shachikov and A. P. Shulyak, “Development of the principles of analysis of the structure of cyclic medical and biologic signals for their detection, recognition, and classification,” Visnyk NTUU “KPI,” Ser. Pryladobuduvannya, Issue 49, 169–179 (2015).

  10. I. V. Lytvynenko, “The problem of segmentation of the cyclic random process with a segmental structure and the approaches to its solving,” J. of Hydrocarbon Power Engineering, Vol. 3, No. 1, 30–37 (2016).

    Google Scholar 

  11. A. I. Povoroznyuk and A. E. Filatova, “Projecting the non-linear filter in the problem of structural identification of biomedical signals with locally concentrated indications,” Systemni Soslidzh. ta Inform. Tekhnologii, No. 1, 69–80 (2014).

  12. L. S. Fainzilberg, Computer Diagnostics based on ECG Phase Portrait [in Russian], Osvita Ukrainy, Kyiv (2013).

    Google Scholar 

  13. A. Bruns, “Fourier-, Hilbert- and wavelet-based signal analysis: Are they really different approaches?” J. of Neuroscience Methods, Vol. 137, Iss. 2, 321–332 (2004). https://doi.org/10.1016/j.jneumeth.2004.03.002.

  14. Chr. Zywienz, D. Borovsky, G. Gotsch, and G. Joseph, “Methodology of ECG interpretation in the Hanover program,” Methods Inf. Med., Vol. 29, 375–385 (1990).

    Article  Google Scholar 

  15. Y. C. Liao, Y. J. Lin, F. P. Chung, S.-L. Chang, L.-W. Lo, Y.-F. Hu, T.-F. Chao, E. Chung, T.-C. Tuan, J.-L. Huang, J.-N. Liao, Y.-Y. Chen, and S.-A. Chen, “Risk stratification of arrhythmogenic right ventricular cardiomyopathy based on signal averaged electrocardiograms,” Int. J. of Cardiology, Vol. 174, No. 3, 628–633 (2014). https://doi.org/10.1016/j.ijcard.2014.04.169.

  16. L. S. Fainzilberg, “Simulated models of generating artificial electrocardiograms under internal and external perturbations,” J. of Qafgaz University, Mathematics and Computer Science, No. 34, 92–104 (2012).

  17. L. L. Frumin and M. B. Shtark, “On the phase portrait of electrocardiogram,” Avtometriya, No. 2, 51–54 (1993).

  18. L. S. Fainzilberg, “Restoration of a standard sample of cyclic waveforms with the use of the Hausdorff metric in a phase space,” Cybern. Syst. Analysis, Vol. 39, No. 3, 338–344 (2003).

    Article  MathSciNet  Google Scholar 

  19. L. S. Fainzilberg, Fundamentals of Phasography [in Russian], Osvita Ukrainy, Kyiv (2017).

    Google Scholar 

  20. T. Jayanthi and M. Anburajan, “Pulse wave velocity and its usefulness in the estimation of hypertension,” Asian J. of Pharmaceutical and Clinical Research, Vol. 10, Iss. 4, 181–187 (2017). https://doi.org/10.22159/ajpcr.2017.v10i4.16447.

    Article  Google Scholar 

  21. L. S. Fainzilberg and E. N. Minina, “Estimating the functional state of cardiovascular system based on the value of scatter of phase trajectories of a one-channel electrocardiogram,” Kibernetika i Vych. Tekhnika, Issue 175, 5–19 (2014).

  22. E. Wesfraid and V. Billat, “Randomness and changes of heart rate and respiratory frequency during high altitude mountain ascent without acclimatization,” Physica A: Statistical Mechanics and Its Applications, Vol. 391, Iss. 4, 1575–1590 (2012). https://doi.org/10.1016/j.physa.2011.08.067.

    Article  Google Scholar 

  23. L. S. Fainzilberg, K. B. Orikhovskaya, and I. V. Vakhovskii, “Estimating the chaoticity of the shape of fragments of a one-channel electrocardiogram,” Kibernetika i Vych. Tekhnika, Issue 183, 4–24 (2016).

    Google Scholar 

  24. L. Fainzilberg, K. Orikhovska, and I. Vakhovskyi, “Analysis of subtle changes in biomedical signals based on entropy phase portrait,” Biomed. Imzheneriya i Elektronika, No. 3, 44–66 (2017).

  25. P. Trahanias and E. Skordalakis, “Syntactic pattern recognition of the ECG,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 12, Iss. 7, 648–657 (1990). https://doi.org/10.1109/34.56207.

  26. V. M. Uspenskiy, “Diagnostic system based on the information analysis of electrocardiogram,” Proc. MECO 2012, Advances and Challenges in Embedded Computing (Bar, Montenegro, June 19–21, 2012), pp. 74–76.

  27. O. V. Kolesnikova and S. S. Krivenko, “Informational analysis of electrocardiosignals: Substantiation and possibilities,” in: Proc. 1st Intern. Sci.-Pract. Conf. Information Systems and Technologies in Medicine (ISM-2018), KhNURE, Kharkiv (2018), pp. 161–163.

  28. R. A. Wagner and M. J. Fischer, “The string-to-string correction problem,” J. of the ACM, Vol. 21, Iss. 1, 168–173 (1974). https://doi.org/10.1145/321796.321811.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. S. Fainzilberg.

Additional information

Translated from Kibernetika i Sistemnyi Analiz, No. 4, July–August, 2020, pp. 172–184

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fainzilberg, L.S. New Approaches to the Analysis and Interpretation of the Shape of Cyclic Signals. Cybern Syst Anal 56, 665–674 (2020). https://doi.org/10.1007/s10559-020-00283-0

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-020-00283-0

Keywords

Navigation