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No Access Submitted: 16 October 2017 Accepted: 08 January 2018 Published Online: 23 January 2018

Characterizing scaling properties of complex signals with missed data segments using the multifractal analysis

Chaos 28, 013124 (2018); https://doi.org/10.1063/1.5009438
A. N. Pavlov1, O. N. Pavlova2, A. S. Abdurashitov2, O. A. Sindeeva3, O. V. Semyachkina-Glushkovskaya3, and J. Kurths4,5
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ABSTRACT
The scaling properties of complex processes may be highly influenced by the presence of various artifacts in experimental recordings. Their removal produces changes in the singularity spectra and the Hölder exponents as compared with the original artifacts-free data, and these changes are significantly different for positively correlated and anti-correlated signals. While signals with power-law correlations are nearly insensitive to the loss of significant parts of data, the removal of fragments of anti-correlated signals is more crucial for further data analysis. In this work, we study the ability of characterizing scaling features of chaotic and stochastic processes with distinct correlation properties using a wavelet-based multifractal analysis, and discuss differences between the effect of missed data for synchronous and asynchronous oscillatory regimes. We show that even an extreme data loss allows characterizing physiological processes such as the cerebral blood flow dynamics.

ACKNOWLEDGMENTS

This work was supported by the Russian Science Foundation (Agreement No. 17-75-20069).

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  1. © 2018 Author(s). Published by AIP Publishing.

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