Multiresolution wavelet analysis of noisy datasets with different measures for decomposition coefficients

The possibility of distinguishing between different types of complex oscillations using datasets contaminated with measurement noise is studied based on multiresolution wavelet analysis (MWA). Unlike the conventional approach, which characterizes the differences in terms of standard deviations of de...

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Bibliographic Details
Published inPhysica A Vol. 585; p. 126406
Main Authors Pavlova, O.N., Guyo, G.A., Pavlov, A.N.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.01.2022
Subjects
Chaotic oscillations
Detrended fluctuation analysis
Multiresolution analysis
Wavelet transform
Detrended fluctuation analysis
Wavelet transform
Multiresolution analysis
Chaotic oscillations
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Summary:The possibility of distinguishing between different types of complex oscillations using datasets contaminated with measurement noise is studied based on multiresolution wavelet analysis (MWA). Unlike the conventional approach, which characterizes the differences in terms of standard deviations of detail wavelet coefficients at independent resolution levels, we consider ways to improve the separation between complex motions by applying several measures for the decomposition coefficients. We show that MWA’s capabilities in diagnosing dynamics can be expanded by applying detrended fluctuation analysis (DFA) to sets of detail wavelet coefficients or by computing the excess of the probability density function of these sets. •The ability to characterize changes in signals with wavelet coefficients is studied.•Advantages of the combined MWA&DFA method are described.•Effects of measurement noise on MWA-based methods are compared.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2021.126406