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Characterizing scaling properties of complex signals with missed data segments using the multifractal analysis
A N Pavlov1, O N Pavlova2, A S Abdurashitov2
1Yuri Gagarin State Technical University of Saratov, Politehnicheskaya Str. 77, 410054 Saratov, Russia.
Chaos (Woodbury, N.Y.)
|February 3, 2018
Summary
Artifacts in experimental data can alter scaling properties. Wavelet multifractal analysis shows that while correlated signals are robust to data loss, anti-correlated signals require careful handling for accurate analysis.
Area of Science:
- Complex systems analysis
- Nonlinear dynamics
- Signal processing
Background:
- Experimental artifacts can significantly influence the scaling properties of complex processes.
- Artifact removal alters singularity spectra and Hölder exponents, with distinct effects on correlated and anti-correlated signals.
- Power-law correlated signals exhibit robustness to data loss, unlike anti-correlated signals.
Purpose of the Study:
- To investigate the characterization of scaling features in chaotic and stochastic processes with varying correlation properties.
- To analyze the impact of missed data on synchronous and asynchronous oscillatory regimes.
- To assess the efficacy of wavelet-based multifractal analysis in handling data loss.
Main Methods:
- Wavelet-based multifractal analysis
- Characterization of scaling properties
- Analysis of correlated and anti-correlated signals
- Simulation of data loss in synchronous and asynchronous regimes
Main Results:
- The study demonstrates that data loss impacts correlated and anti-correlated signals differently.
- Wavelet multifractal analysis can effectively characterize scaling features even with significant data loss.
- Physiological processes like cerebral blood flow dynamics can be characterized despite extreme data loss.
Conclusions:
- The presence of artifacts and data loss critically affects the analysis of complex system dynamics.
- Wavelet multifractal analysis provides a robust method for characterizing scaling properties in the presence of data loss.
- This approach is applicable to real-world physiological data, such as cerebral blood flow, even under adverse data conditions.
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