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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Detecting chronotaxic systems from single-variable time series with separable amplitude and phase
AU - Lancaster, Gemma
AU - Clemson, Philip
AU - Suprunenko, Yevhen
AU - Stankovski, Tomislav
AU - Stefanovska, Aneta
PY - 2015/6/23
Y1 - 2015/6/23
N2 - The recent introduction of chronotaxic systems provides the means to describe nonautonomous systems with stable yet time-varying frequencies which are resistant to continuous external perturbations. This approach facilitates realistic characterization of the oscillations observed in living systems, including the observation of transitions in dynamics which were not considered previously. The novelty of this approach necessitated the development of a new set of methods for the inference of the dynamics and interactions present in chronotaxic systems. These methods, based on Bayesian inference and detrended fluctuation analysis, can identify chronotaxicity in phase dynamics extracted from a single time series. Here, they are applied to numerical examples and real experimental EEG data. We also review the current methods, including their assumptions and limitations, elaborate on their implementation, and discuss future perspectives.
AB - The recent introduction of chronotaxic systems provides the means to describe nonautonomous systems with stable yet time-varying frequencies which are resistant to continuous external perturbations. This approach facilitates realistic characterization of the oscillations observed in living systems, including the observation of transitions in dynamics which were not considered previously. The novelty of this approach necessitated the development of a new set of methods for the inference of the dynamics and interactions present in chronotaxic systems. These methods, based on Bayesian inference and detrended fluctuation analysis, can identify chronotaxicity in phase dynamics extracted from a single time series. Here, they are applied to numerical examples and real experimental EEG data. We also review the current methods, including their assumptions and limitations, elaborate on their implementation, and discuss future perspectives.
U2 - 10.3390/e17064413
DO - 10.3390/e17064413
M3 - Journal article
VL - 17
SP - 4413
EP - 4438
JO - Entropy
JF - Entropy
IS - 6
ER -