Reconstructing Time-Dependent Dynamics
Reconstructing Time-Dependent Dynamics
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DOI：
10.1109/jproc.2015.2491262
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发表时间：
2016-02-01
影响因子：
20.6
通讯作者：
Stefanovska, Aneta
中科院分区：
文献类型：
--
作者：
Clemson, Philip;Lancaster, Gemma;Stefanovska, Aneta
生物医学数据中所含信息的有用性在很大程度上取决于提取方法的可靠性和准确性。传统的平稳性和自主性假设在生命系统中被打破，因为它们在热力学上是开放的，因此不断地与环境相互作用。这导致了固有的时变性，并导致高度非线性、时变动力学。信号分析的目的通常是深入了解产生信号的系统的行为。在这里，提出了一系列的信号分析方法，并应用于提取时变振荡模式及其相互作用的信息。讨论了表征信号及其潜在非自治动力学的方法，包括时频分析、分解、相干分析和动态贝叶斯推理，以研究相互作用和耦合函数。通过对心血管和脑电图数据的应用说明了这些方法。最近引入的时差系统提供了一个理论框架，其中动态系统可以具有时变的振幅和频率，但保持稳定，很好地匹配生命的特征。我们证明，当应用于慢速系统时，所提出的方法有助于在许多时间和空间尺度上准确提取系统动力学。
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.