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...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/1/93 |
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| author | Alexander A. Koronovskii Inna A. Blokhina Alexander V. Dmitrenko Matvey A. Tuzhilkin Tatyana V. Moiseikina Inna V. Elizarova Oxana V. Semyachkina-Glushkovskaya Alexey N. Pavlov |
| author_facet | Alexander A. Koronovskii Inna A. Blokhina Alexander V. Dmitrenko Matvey A. Tuzhilkin Tatyana V. Moiseikina Inna V. Elizarova Oxana V. Semyachkina-Glushkovskaya Alexey N. Pavlov |
| author_sort | Alexander A. Koronovskii |
| collection | DOAJ |
| description | 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 <i>t</i>-values up to 34% was revealed. |
| first_indexed | 2024-03-11T10:04:56Z |
| format | Article |
| id | doaj.art-6e93342c66564f5b9dc66293d780b655 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T10:04:56Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-6e93342c66564f5b9dc66293d780b6552023-11-16T15:08:44ZengMDPI AGDiagnostics2075-44182022-12-011319310.3390/diagnostics13010093Extended Detrended Fluctuation Analysis of Coarse-Grained Time SeriesAlexander A. Koronovskii0Inna A. Blokhina1Alexander V. Dmitrenko2Matvey A. Tuzhilkin3Tatyana V. Moiseikina4Inna V. Elizarova5Oxana V. Semyachkina-Glushkovskaya6Alexey N. Pavlov7Physics of Open Systems Department, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, RussiaDepartment of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, RussiaDepartment of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, RussiaDepartment of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, RussiaDepartment of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, RussiaDepartment of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, RussiaDepartment of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, RussiaPhysics of Open Systems Department, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, RussiaA 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 <i>t</i>-values up to 34% was revealed.https://www.mdpi.com/2075-4418/13/1/93fluctuation analysisscaling exponentelectrical brain activitysignal processingdiagnostics |
| spellingShingle | Alexander A. Koronovskii Inna A. Blokhina Alexander V. Dmitrenko Matvey A. Tuzhilkin Tatyana V. Moiseikina Inna V. Elizarova Oxana V. Semyachkina-Glushkovskaya Alexey N. Pavlov Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series Diagnostics fluctuation analysis scaling exponent electrical brain activity signal processing diagnostics |
| title | Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series |
| title_full | Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series |
| title_fullStr | Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series |
| title_full_unstemmed | Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series |
| title_short | Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series |
| title_sort | extended detrended fluctuation analysis of coarse grained time series |
| topic | fluctuation analysis scaling exponent electrical brain activity signal processing diagnostics |
| url | https://www.mdpi.com/2075-4418/13/1/93 |
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