| Autor: |
Koronovskii AA Jr; Physics of Open Systems Department, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia., Blokhina IA; Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia., Dmitrenko AV; Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia., Tuzhilkin MA; Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia., Moiseikina TV; Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia., Elizarova IV; Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia., Semyachkina-Glushkovskaya OV; Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia., Pavlov AN; Physics of Open Systems Department, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia. |
| Abstrakt: |
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. |
