Next Article in Journal
Analysis of the Keller–Segel Model with a Fractional Derivative without Singular Kernel
Next Article in Special Issue
Reliability Analysis Based on a Jump Diffusion Model with Two Wiener Processes for Cloud Computing with Big Data
Previous Article in Journal
Intransitivity in Theory and in the Real World
Previous Article in Special Issue
Brownian Motion in Minkowski Space
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(6), 4413-4438; doi:10.3390/e17064413

Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and Phase

1
Department of Physics, Lancaster University, LA1 4YB, Lancaster, UK
2
Institute of Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cafaro
Received: 22 April 2015 / Revised: 3 June 2015 / Accepted: 10 June 2015 / Published: 23 June 2015
(This article belongs to the Special Issue Dynamical Equations and Causal Structures from Observations)
View Full-Text   |   Download PDF [2141 KB, uploaded 23 June 2015]   |  

Abstract

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. View Full-Text
Keywords: chronotaxic systems; inverse approach; nonautonomous dynamical systems; Bayesian inference; detrended fluctuation analysis chronotaxic systems; inverse approach; nonautonomous dynamical systems; Bayesian inference; detrended fluctuation analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lancaster, G.; Clemson, P.T.; Suprunenko, Y.F.; Stankovski, T.; Stefanovska, A. Detecting Chronotaxic Systems from Single-Variable Time Series with Separable Amplitude and Phase. Entropy 2015, 17, 4413-4438.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top