Home > Research > Publications & Outputs > Detecting chronotaxic systems from single-varia...

Electronic data

  • entropy2

    Accepted author manuscript, 2.17 MB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License


Text available via DOI:

View graph of relations

Detecting chronotaxic systems from single-variable time series with separable amplitude and phase

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>23/06/2015
Issue number6
Number of pages26
Pages (from-to)4413-4438
Publication StatusPublished
<mark>Original language</mark>English


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 EEG data. We also review the current methods, including their assumptions and limitations, elaborate on their implementation, and discuss future perspectives.