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  Extended detrended fluctuation analysis: effects of nonstationarity and application to sleep data

Pavlov, A. N., Pavlova, O. N., Semyachkina-Glushkovskaya, O. V., Kurths, J. (2021): Extended detrended fluctuation analysis: effects of nonstationarity and application to sleep data. - European Physical Journal Plus, 136, 1, 10.
https://doi.org/10.1140/epjp/s13360-020-00980-x

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Pavlov, A. N.1, Author
Pavlova, O. N.1, Author
Semyachkina-Glushkovskaya, O. V.1, Author
Kurths, Jürgen2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Extended detrended fluctuation analysis (EDFA) is a recently proposed modification of the conventional method, which provides a characterization of complex time series with varying nonstationarity. It evaluates two scaling exponents for a better quantification of inhomogeneous datasets. Here, we study the effect of different types of nonstationarity on these exponents, including trend, switching between processes with distinct statistical properties and energy variability. Using the simulated signals, we show that the first two types of nonstationarity have the strongest effect for anticorrelated processes and complicate their diagnosis. Nonstationarity in energy is more crucial for time series with positive long-range correlations. Next, we apply EDFA to rat experiments to study the activation of brain lymphatic drainage during sleep. Our analysis reveals significant distinctions in EDFA’s measures between the background electrical activity of the brain and the stage of sleep. The latter offers an indirect way to identify and characterize the nightly activation of the drainage and clearance of brain tissue.

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 Dates: 2021-01-022021-01
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1140/epjp/s13360-020-00980-x
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
MDB-ID: No data to archive
Research topic keyword: Health
Model / method: Nonlinear Data Analysis
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Title: European Physical Journal Plus
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 136 (1) Sequence Number: 10 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals139
Publisher: Springer