Detecting nonlinearity in short and noisy time series using the permutation entropy

Zunino, Luciano; Kulp, Christopher W.

doi:10.1016/j.physleta.2017.09.032

Cited by 25 publications

(17 citation statements)

References 54 publications

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“…m , for which positive values indicate the presence of structure in the data. This usage stems from recent results demonstrating the successful distinction of chaos and noise based on the systematic comparison to surrogate timeseries [97]. We retained the setting m = 5, but allowed the lag to span d = 1, .…”

Section: Stochastic Spiking Systems a Simulated Neuronal Microcircuitmentioning

confidence: 99%

Connectivity Influences on Nonlinear Dynamics in Weakly-Synchronized Networks: Insights From Rössler Systems, Electronic Chaotic Oscillators, Model and Biological Neurons

et al. 2019

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Natural and engineered networks, such as interconnected neurons, ecological and social networks, coupled oscillators, wireless terminals and power loads, are characterized by an appreciable heterogeneity in the local connectivity around each node. For instance, in both elementary structures such as stars and complex graphs having scale-free topology, a minority of elements are linked to the rest of the network disproportionately strongly. While the effect of the arrangement of structural connections on the emergent synchronization pattern has been studied extensively, considerably less is known about its influence on the temporal dynamics unfolding within each node. Here, we present a comprehensive investigation across diverse simulated and experimental systems, encompassing star and complex networks of Rössler systems, coupled hysteresis-based electronic oscillators, microcircuits of leaky integrate-and-fire model neurons, and finally recordings from in-vitro cultures of spontaneously-growing neuronal networks. We systematically consider a range of dynamical measures, including the correlation dimension, nonlinear prediction error, permutation entropy, and other information-theoretical indices. The empirical evidence gathered reveals that under situations of weak synchronization, wherein rather than a collective behavior one observes significantly differentiated dynamics, denser connectivity tends to locally promote the emergence of stronger signatures of nonlinear dynamics. In deterministic systems, transition to chaos and generation of higher-dimensional signals were observed; however, when the coupling is stronger, this relationship may be lost or even inverted. In systems with a strong stochastic component, the generation of more temporally-organized activity could be induced. These observations have many potential implications across diverse fields of basic and applied science, for example, in the design of distributed sensing systems based on wireless coupled oscillators, in network identification and control, as well as in the interpretation of neuroscientific and other dynamical data.

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Section: Stochastic Spiking Systems a Simulated Neuronal Microcircuitmentioning

confidence: 99%

Connectivity Influences on Nonlinear Dynamics in Weakly-Synchronized Networks: Insights From Rössler Systems, Electronic Chaotic Oscillators, Model and Biological Neurons

et al. 2019

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“…structure of a sequence, inherits its causal information [35], and it is not affected by nonlinear monotonous transformations that could be introduced by the glucose monitors. It can be applied to deterministic or stochastic systems without any assumptions about the underlying process [36].…”

Section: Permutation Entropymentioning

confidence: 99%

Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm

Cuesta–Frau

Miró-Martínez

Oltra-Crespo

et al. 2018

Computer Methods and Programs in Biomedicine

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights• First time Permutation Entropy is applied to glucose time series.• Test of different customizations for Permutation Entropy in order to address equal values and amplitude variations.• Prediction of evolution to diabetes based on a Permutation Entropy analysis of the glucose time series.

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“…Other followers [37,38] adopting these groups of continuous EEGs share the outcomes that the epileptic seizure-free EEGs nonlinearity is higher than the healthy volunteers. Our findings, however, challenge the reports and suggest the healthy subjects should have more dynamical activities than the epileptics in their seizure-free intervals.…”

Section: Transfer Entropy Based On Permutationmentioning

confidence: 94%

Networked information interactions of epileptic EEG based on symbolic transfer entropy

Yao

Wang

2019

Preprint

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Identifying networked information exchanges among brain regions is important for understanding the brain structure. We employ symbolic transfer entropy to facilitate the construction of networked information interactions for EEGs of 22 epileptics and 22 healthy subjects. The epileptic patients during seizure-free interval have lower information transfer in each individual and whole brain regions than the healthy subjects.Among all of the brain regions, the information flows out of and into the brain area of O1 of the epileptic EEGs are significantly lower than those of the healthy (p<0.0005), and the information flow from F7 to F8 (p<0.00001) is particularly promising to discriminate the two groups of EEGs. Moreover, Shannon entropy of probability distributions of information exchanges suggests that the healthy EEGs have higher complexity and irregularity than the epileptic brain electrical activities. By characterizing the brain networked information interactions, our findings highlight the long-term reduced information exchanges, degree of brain interactivities and informational complexity of the epileptic EEG.

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Detecting nonlinearity in short and noisy time series using the permutation entropy

Cited by 25 publications

References 54 publications

Connectivity Influences on Nonlinear Dynamics in Weakly-Synchronized Networks: Insights From Rössler Systems, Electronic Chaotic Oscillators, Model and Biological Neurons

Connectivity Influences on Nonlinear Dynamics in Weakly-Synchronized Networks: Insights From Rössler Systems, Electronic Chaotic Oscillators, Model and Biological Neurons

Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm

Networked information interactions of epileptic EEG based on symbolic transfer entropy

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