Home > Research > Publications & Outputs > The inverse approach to chronotaxic systems for...

Associated organisational unit

Electronic data

  • Clemson (2014) PhysRevE.89.032904

    Rights statement: Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

    Final published version, 1.76 MB, PDF document

    Available under license: CC BY

Links

Text available via DOI:

View graph of relations

The inverse approach to chronotaxic systems for single-variable time series

Research output: Contribution to journalJournal article

Published

Standard

The inverse approach to chronotaxic systems for single-variable time series. / Clemson, Philip; Suprunenko, Yevhen; Stankovski, Tomislav; Stefanovska, Aneta.

In: Physical Review E, Vol. 89, No. 3, 032904, 10.03.2014.

Research output: Contribution to journalJournal article

Harvard

APA

Vancouver

Author

Clemson, Philip ; Suprunenko, Yevhen ; Stankovski, Tomislav ; Stefanovska, Aneta. / The inverse approach to chronotaxic systems for single-variable time series. In: Physical Review E. 2014 ; Vol. 89, No. 3.

Bibtex

@article{1884750ea74847fca1caa37161b3010d,
title = "The inverse approach to chronotaxic systems for single-variable time series",
abstract = "Following the development of a new class of self-sustained oscillators with a time-varying but stable frequency, the inverse approach to these systems is now formulated. We show how observed data arranged in a single-variable time series can be used to recognise such systems. This approach makes use of time-frequency domain information using the wavelet transform as well as the recently-developed method of Bayesian-based inference. In addition, a new set of methods, named phase fluctuation analysis, is introduced to detect the defining properties of the new class of systems by directly analysing the statistics of the observed perturbations. We apply these methods to numerical examples but also elaborate further on the cardiac system.",
author = "Philip Clemson and Yevhen Suprunenko and Tomislav Stankovski and Aneta Stefanovska",
note = "Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.",
year = "2014",
month = "3",
day = "10",
doi = "10.1103/PhysRevE.89.032904",
language = "English",
volume = "89",
journal = "Physical Review E",
issn = "1539-3755",
publisher = "American Physical Society",
number = "3",

}

RIS

TY - JOUR

T1 - The inverse approach to chronotaxic systems for single-variable time series

AU - Clemson, Philip

AU - Suprunenko, Yevhen

AU - Stankovski, Tomislav

AU - Stefanovska, Aneta

N1 - Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

PY - 2014/3/10

Y1 - 2014/3/10

N2 - Following the development of a new class of self-sustained oscillators with a time-varying but stable frequency, the inverse approach to these systems is now formulated. We show how observed data arranged in a single-variable time series can be used to recognise such systems. This approach makes use of time-frequency domain information using the wavelet transform as well as the recently-developed method of Bayesian-based inference. In addition, a new set of methods, named phase fluctuation analysis, is introduced to detect the defining properties of the new class of systems by directly analysing the statistics of the observed perturbations. We apply these methods to numerical examples but also elaborate further on the cardiac system.

AB - Following the development of a new class of self-sustained oscillators with a time-varying but stable frequency, the inverse approach to these systems is now formulated. We show how observed data arranged in a single-variable time series can be used to recognise such systems. This approach makes use of time-frequency domain information using the wavelet transform as well as the recently-developed method of Bayesian-based inference. In addition, a new set of methods, named phase fluctuation analysis, is introduced to detect the defining properties of the new class of systems by directly analysing the statistics of the observed perturbations. We apply these methods to numerical examples but also elaborate further on the cardiac system.

U2 - 10.1103/PhysRevE.89.032904

DO - 10.1103/PhysRevE.89.032904

M3 - Journal article

VL - 89

JO - Physical Review E

JF - Physical Review E

SN - 1539-3755

IS - 3

M1 - 032904

ER -