Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and Phase
The recent introduction of chronotaxic systems provides the means to describe nonautonomous systems with stable yet time-varying frequencies which are resistant to continuous external perturbations. This approach facilitates realistic characterization of the oscillations observed in living systems,...
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doaj-7760bcc1aac1476dbdd9e09894b4ad612020-11-25T00:09:44ZengMDPI AGEntropy1099-43002015-06-011764413443810.3390/e17064413e17064413Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and PhaseGemma Lancaster0Philip T. Clemson1Yevhen F. Suprunenko2Tomislav Stankovski3Aneta Stefanovska4Department of Physics, Lancaster University, LA1 4YB, Lancaster, UKDepartment of Physics, Lancaster University, LA1 4YB, Lancaster, UKDepartment of Physics, Lancaster University, LA1 4YB, Lancaster, UKDepartment of Physics, Lancaster University, LA1 4YB, Lancaster, UKDepartment of Physics, Lancaster University, LA1 4YB, Lancaster, UKThe recent introduction of chronotaxic systems provides the means to describe nonautonomous systems with stable yet time-varying frequencies which are resistant to continuous external perturbations. This approach facilitates realistic characterization of the oscillations observed in living systems, including the observation of transitions in dynamics which were not considered previously. The novelty of this approach necessitated the development of a new set of methods for the inference of the dynamics and interactions present in chronotaxic systems. These methods, based on Bayesian inference and detrended fluctuation analysis, can identify chronotaxicity in phase dynamics extracted from a single time series. Here, they are applied to numerical examples and real experimental electroencephalogram (EEG) data. We also review the current methods, including their assumptions and limitations, elaborate on their implementation, and discuss future perspectives.http://www.mdpi.com/1099-4300/17/6/4413chronotaxic systemsinverse approachnonautonomous dynamical systemsBayesian inferencedetrended fluctuation analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gemma Lancaster Philip T. Clemson Yevhen F. Suprunenko Tomislav Stankovski Aneta Stefanovska |
spellingShingle |
Gemma Lancaster Philip T. Clemson Yevhen F. Suprunenko Tomislav Stankovski Aneta Stefanovska Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and Phase Entropy chronotaxic systems inverse approach nonautonomous dynamical systems Bayesian inference detrended fluctuation analysis |
author_facet |
Gemma Lancaster Philip T. Clemson Yevhen F. Suprunenko Tomislav Stankovski Aneta Stefanovska |
author_sort |
Gemma Lancaster |
title |
Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and Phase |
title_short |
Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and Phase |
title_full |
Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and Phase |
title_fullStr |
Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and Phase |
title_full_unstemmed |
Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and Phase |
title_sort |
detecting chronotaxic systems from single-variable time series with separable amplitude and phase |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2015-06-01 |
description |
The recent introduction of chronotaxic systems provides the means to describe nonautonomous systems with stable yet time-varying frequencies which are resistant to continuous external perturbations. This approach facilitates realistic characterization of the oscillations observed in living systems, including the observation of transitions in dynamics which were not considered previously. The novelty of this approach necessitated the development of a new set of methods for the inference of the dynamics and interactions present in chronotaxic systems. These methods, based on Bayesian inference and detrended fluctuation analysis, can identify chronotaxicity in phase dynamics extracted from a single time series. Here, they are applied to numerical examples and real experimental electroencephalogram (EEG) data. We also review the current methods, including their assumptions and limitations, elaborate on their implementation, and discuss future perspectives. |
topic |
chronotaxic systems inverse approach nonautonomous dynamical systems Bayesian inference detrended fluctuation analysis |
url |
http://www.mdpi.com/1099-4300/17/6/4413 |
work_keys_str_mv |
AT gemmalancaster detectingchronotaxicsystemsfromsinglevariabletimeserieswithseparableamplitudeandphase AT philiptclemson detectingchronotaxicsystemsfromsinglevariabletimeserieswithseparableamplitudeandphase AT yevhenfsuprunenko detectingchronotaxicsystemsfromsinglevariabletimeserieswithseparableamplitudeandphase AT tomislavstankovski detectingchronotaxicsystemsfromsinglevariabletimeserieswithseparableamplitudeandphase AT anetastefanovska detectingchronotaxicsystemsfromsinglevariabletimeserieswithseparableamplitudeandphase |
_version_ |
1725410414322253824 |