TY - JOUR

T1 - Reconstructing time-dependent dynamics

AU - Clemson, Philip

AU - Lancaster, Gemma

AU - Stefanovska, Aneta

N1 - (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.

PY - 2016/2

Y1 - 2016/2

N2 - 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 systemsbreak 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 manyscales.

AB - 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 systemsbreak 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 manyscales.

U2 - 10.1109/JPROC.2015.2491262

DO - 10.1109/JPROC.2015.2491262

M3 - Journal article

VL - 104

SP - 223

EP - 241

JO - Proceedings of the IEEE

JF - Proceedings of the IEEE

SN - 0018-9219

IS - 2

ER -