Abstract
The problem of diagnostics of structural variations in nonstationary processes is considered in the case of a degree of nonstationarity that is dependent on the frequency range. Multiscale analysis of experimental data on rhythmic processes with time-varying characteristics is carried out by the example of sleep slow wave dynamics. Possibility of improving the quality of diagnostics by selecting proper wavelet basis set functions is discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
REFERENCES
J. S. Bendat and A. G. Piersol, Random Data: Analysis and Measurement Procedures, 4th ed. (Wiley India, New Delhi, 2013).
P. Bernaola-Galvan, P. Ch. Ivanov, L. A. N. Amaral, and H. E. Stanley, Phys. Rev. Lett. 87, 168105 (2001).
H. D. Jennings, P. Ch. Ivanov, A. M. Martins, P. C. Silva, and G. M. Vishwanathan, Phys. A (Amsterdam) 336, 585 (2004).
A. A. Koronovskii, V. A. Makarov, A. N. Pavlov, E. Yu. Sitnikova, and A. E. Khramov, Wavelets in Neurodynamics and Neurophysiology (Fizmatlit, Moscow, 2013) [in Russian].
J. A. Schulte, Nonlin. Process. Geophys. 23, 257 (2016).
N. S. Frolov, V. V. Grubov, V. A. Maksimenko, A. Luttjohann, V. V. Makarov, A. N. Pavlov, E. Sitnikova, A. N. Pisarchik, J. Kurths, and A. E. Hramov, Sci. Rep. 9, 7243 (2019).
I. Daubechies, Ten Lectures on Wavelets (SIAM, Philadelphia, 1992).
V. V. Grubov, A. E. Runnova, A. A. Koronovskii, and A. E. Hramov, Tech. Phys. Lett. 43, 619 (2017).
S. V. Bozhokin, S. V. Zharko, N. V. Larionov, A. N. Litvinov, and I. M. Sokolov, Tech. Phys. 62, 837 (2017).
S. V. Bozhokin and I. M. Sokolov, Tech. Phys. 63, 1711 (2018).
O. N. Pavlova and A. N. Pavlov, Tech. Phys. Lett. 44, 640 (2018).
D. S. Grishina, A. N. Pavlov, O. N. Pavlova, and A. E. Runnova, Tech. Phys. Lett. 45, 820 (2019).
S. Thurner, M. C. Feurstein, and M. C. Teich, Phys. Rev. Lett. 80, 1544 (1998).
E. Bresch, U. Großekathofer, and G. Garcia-Molina, Front. Comput. Neurosci. 12, 85 (2018).
G. Garcia-Molina, T. Tsoneva, J. Jasko, B. Steele, A. Aquino, K. Baher, S. Pastoor, S. Pfundtner, L. Ostrowski, B. Miller, N. Papas, B. Riedner, G. Tononi, and D. P. White, J. Neural Eng. 15, 066018 (2018).
R. P. Bartsch, A. Y. Schumann, J. W. Kantelhardt, T. Penzel, and P. Ch. Ivanov, Proc. Natl. Acad. Sci. U. S. A. 109, 10181 (2012).
K. K. L. Liu, R. P. Bartsch, A. Lin, R. N. Mantegna, and P. Ch. Ivanov, Front. Neural Circuits 9, 62 (2015).
ACKNOWLEDGMENTS
The authors are grateful to O.V. Ivanov for kindly providing 64-bit coefficients of wavelet filters of the Daubechies family and for fruitful discussions.
Funding
This work was supported by the RF Government (grant no. 075-15-2019-1885). A.N.P. acknowledges support by the grant of the President of the Russian Federation for leading scientific schools (no. NSh-2594.2020.2) and by the Mathematical Center of the Saratov State University.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
COMPLIANCE WITH ETHICAL STANDARDS
All studies involving human beings were conducted in accordance with principles for human experimentation as defined in the International Conference on Harmonization Good Clinical Practice Guidelines, the National Committee on Research Ethics, the 1964 Helsinki Declaration, and later amendments or comparable ethical standards. Informed consent was obtained from each participant included in the study.
CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
Additional information
Translated by P. Pozdeev
Rights and permissions
About this article
Cite this article
Kupriyashkina, N.M., Pavlova, O.N. & Pavlov, A.N. Multiscale Analysis of Rhythmic Processes with Time-Varying Characteristics. Tech. Phys. Lett. 46, 893–895 (2020). https://doi.org/10.1134/S1063785020090217
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1063785020090217