Home > Research > Publications & Outputs > Reconstructing time-dependent dynamics

Electronic data

  • IEEE

    Rights statement: (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

    Accepted author manuscript, 14.2 MB, PDF document

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

Links

Text available via DOI:

View graph of relations

Reconstructing time-dependent dynamics

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>02/2016
<mark>Journal</mark>Proceedings of the IEEE
Issue number2
Volume104
Number of pages19
Pages (from-to)223-241
Publication statusPublished
Early online date19/01/16
Original languageEnglish

Abstract

The usefulness of the information extracted from biomedical data relies heavily on the underlying theory of the methods used in its extraction. The assumptions of stationarity and autonomicity traditionally applied to dynamical systems
break down when considering living systems, due to their inherent time-variability. Living systems are thermodynamically open, and thus constantly interacting with their environment. This results in highly nonlinear, time-dependent dynamics. The aim of signal analysis is to gain insight into the behaviour of the system from which the signal originated. Here, various analysis methods for the characterization of signals and their underlying non-autonomous dynamics are presented, incorporating time-frequency analysis, time-domain decomposition of nonlinear modes, and methods to study mutual interactions and couplings using dynamical Bayesian inference, wavelet-bispectral and time-localised coherence, and entropy and information-based analysis. The recent introduction of chronotaxic systems provides a theoretical framework in which dynamical systems can have amplitudes and frequencies which are time-varying, yet stable, matching well the characteristics of living systems. We demonstrate that considering this theory of chronotaxic systems whilst applying the presented methods results in an approach for the reconstruction of the dynamics of living systems across many
scales.

Bibliographic note

(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.