Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series

A coarse-graining procedure, which involves averaging time series in non-overlapping windows followed by processing of the obtained multiple data sets, is the initial step in the multiscale entropy computation method. In this paper, we discuss how this procedure can be applied with other methods of time series analysis. Based on extended detrended fluctuation analysis (EDFA), we compare signal processing results for data sets with and without coarse-graining. Using the simulated data provided by the interacting nephrons model, we show how this procedure increases, up to 48%, the distinctions between local scaling exponents quantifying synchronous and asynchronous chaotic oscillations. Based on the experimental data of electrocorticograms (ECoG) of mice, an improvement in differences in local scaling exponents up to 41% and Student's t-values up to 34% was revealed.

Automated summary

In this paper, we discuss how the coarse-graining procedure can be used in combination with other time series analysis methods. Signal processing results for data sets with and without coarse-graining are compared using extended detrended fluctuation analysis. The procedure is shown to increase the distinctions between local scaling exponents quantifying synchronous and asynchronous chaotic oscillations by up to 48% using simulated data and up to 41% in experimental data of electrocorticograms (ECoG) of mice.