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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Main Authors: Gemma Lancaster, Philip T. Clemson, Yevhen F. Suprunenko, Tomislav Stankovski, Aneta Stefanovska
Format: Article
Language:English
Published: MDPI AG 2015-06-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/17/6/4413
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spelling 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
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AT yevhenfsuprunenko detectingchronotaxicsystemsfromsinglevariabletimeserieswithseparableamplitudeandphase
AT tomislavstankovski detectingchronotaxicsystemsfromsinglevariabletimeserieswithseparableamplitudeandphase
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