Reconstructing Time-Dependent Dynamics

The usefulness of the information contained in biomedical data relies heavily on the reliability and accuracy of the methods used for its extraction. The conventional assumptions of stationarity and autonomicity break down in the case of living systems because they are thermodynamically open, and th...

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Bibliographic Details
Published in:Proceedings of the IEEE 2016-02, Vol.104 (2), p.223-241
Main Authors: Clemson, Philip, Lancaster, Gemma, Stefanovska, Aneta
Format: Article
Language:English
Subjects:
Biomedical signal processing
Complexity theory
Couplings
Dynamical systems
Dynamics
Extraction
Inference
Nonlinear dynamical systems
Nonlinear dynamics
Nonlinearity
Oscillators
Signal analysis
System dynamics
Time series analysis
Time-frequency analysis
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Summary:The usefulness of the information contained in biomedical data relies heavily on the reliability and accuracy of the methods used for its extraction. The conventional assumptions of stationarity and autonomicity break down in the case of living systems because they are thermodynamically open, and thus constantly interacting with their environments. This leads to an inherent time-variability and results in highly nonlinear, time-dependent dynamics. The aim of signal analysis usually is to gain insight into the behavior of the system from which the signal originated. Here, a range of signal analysis methods is presented and applied to extract information about time-varying oscillatory modes and their interactions. Methods are discussed for the characterization of signals and their underlying nonautonomous dynamics, including time-frequency analysis, decomposition, coherence analysis and dynamical Bayesian inference to study interactions and coupling functions. They are illustrated by being applied to cardiovascular and EEG data. The recent introduction of chronotaxic systems provides a theoretical framework within which dynamical systems can have amplitudes and frequencies which are time-varying, yet remain stable, matching well the characteristics of life. We demonstrate that, when applied in the context of chronotaxic systems, the methods presented facilitate the accurate extraction of the system dynamics over many scales of time and space.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2015.2491262