Detecting dynamical changes in time series using the permutation entropy

Cao, Yinhe; Tung, Wen‐wen; Gao, Jianbo; Protopopescu, V.; Hively, L.M.

doi:10.1103/physreve.70.046217

Cited by 471 publications

(325 citation statements)

References 37 publications

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“…. ; m!Þ to denote the probability distribution of permutation p j , the PE for this scalar series was defined as (Cao et al 2004;Li et al 2010;Yi et al 2014)…”

Section: Pe and Oimentioning

confidence: 99%

“…Earlier studies have shown that this kind of complexity measures is particularly suitable for non-stationary time series (Cao et al 2004;Keller et al 2014). They have been successfully applied to analyze epileptic and anesthetic EEG data (Ouyang et al 2010;Cao et al 2004;Keller et al 2014;Li et al 2007Li et al , 2008Li et al , 2010. In our previous study (Yi et al 2014), we have also used them to detect the complexity changes of EEG activity associated with manual acupuncture at acupoint ST 36 in healthy subjects.…”

Section: Introductionmentioning

confidence: 99%

“…That is, a recorded data with high complexity corresponds to a larger PE and lower OI, whereas a low complexity leads to a lower PE and larger OI. Earlier studies have shown that this kind of complexity measures is particularly suitable for non-stationary time series (Cao et al 2004;Keller et al 2014). They have been successfully applied to analyze epileptic and anesthetic EEG data (Ouyang et al 2010;Cao et al 2004;Keller et al 2014;Li et al 2007Li et al , 2008Li et al , 2010.…”

Section: Introductionmentioning

confidence: 99%

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Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Wang

Deng

et al. 2016

Cogn Neurodyn

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To investigate the abnormal brain activities in the early stage of Parkinson's disease (PD), the electroencephalogram (EEG) signals were recorded with 20 channels from non-dementia PD patients (18 patients, 8 females) and age matched healthy controls (18 subjects, 8 females) during the resting state. Two methods based on the ordinal patterns of the recorded series, i.e., permutation entropy (PE) and order index (OI), were introduced to characterize the complexity of the cortical activities for two groups. It was observed that the resting-state EEG of PD patients showed lower PE and higher OI than healthy controls, which indicated that the early-stage PD caused the reduced complexity of EEG. We further applied two methods to determine the complexity of EEG rhythms in five sub-bands. The results showed that the gamma, beta and alpha rhythms of PD patients were characterized by lower PE and higher OI, i.e., reduced complexity, than healthy subjects. No significant differences were observed in theta or delta rhythms between two groups. The findings suggested that PE and OI were promising methods to detect the abnormal changes in the dynamics of EEG signals associated with early-stage PD. Further, such changes in EEG complexity may be the early markers of the cortical or subcortical dysfunction caused by PD.

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“…. ; m!Þ to denote the probability distribution of permutation p j , the PE for this scalar series was defined as (Cao et al 2004;Li et al 2010;Yi et al 2014)…”

Section: Pe and Oimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Wang

Deng

et al. 2016

Cogn Neurodyn

View full text Add to dashboard Cite

show abstract

“…In particular, the accurate detection of transitions from a normal to an abnormal state, either due to hardware or software failure, or due to an attack, may improve diagnosis and treatment. The multi-scale decomposition given by the SSA approach, could be combined with the conceptually simple and computationally very fast concept of permutation entropy [26] to detect dynamical changes in the subset of noisy eigenloads, which are responsible for the transient behavior of the traffic load series. Besides, to encourage further experimentation, we have made our datasets available to the research community [31].…”

Section: Discussionmentioning

confidence: 99%

Trend forecasting based on Singular Spectrum Analysis of traffic workload in a large-scale wireless LAN

Tzagkarakis¹,

Papadopouli²,

Tsakalides³

2009

Performance Evaluation

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a b s t r a c tNetwork traffic load in an IEEE802.11 infrastructure arises from the superposition of traffic accessed by wireless clients associated with access points (APs). An accurate load characterization can be beneficial in modeling network traffic and addressing a variety of problems including coverage planning, resource reservation and network monitoring for anomaly detection. This study focuses on the statistical analysis of the traffic load measured in a campus-wide IEEE802.11 infrastructure at each AP.Using the Singular Spectrum Analysis approach, we found that the time-series of traffic load at a given AP has a small intrinsic dimension. In particular, these time-series can be accurately modeled using a small number of leading (principal) components. This proved to be critical for understanding the main features of the components forming the network traffic.Statistical analysis of leading components has demonstrated that even a few first components form the main part of the information. The residual components capture the small irregular variations, which do not fit in the basic part of the network traffic and can be interpreted as a stochastic noise. Based on these properties, we also studied contributions of the various components to the overall structure of the traffic load of an AP and its variation over time.Finally, we designed and evaluated the performance of a traffic predictor for the trend component, obtained by projecting the original time-series on the set of leading components.

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“…Complexity measures have been shown to be powerful tools for detecting dynamical changes in time series from epileptic patients [27] and in speech signals [20,28], for distinguishing chaotic signals from stochastic ones, for distinguishing among different degrees of stochasticity [29], for quantifying stochastic and coherence resonances [30], and for classifying spatio-temporal patterns [31,32] and neural networks [33,34] etc.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the complexity of the delayed logistic map

Masoller

Rosso

2011

Phil. Trans. R. Soc. A.

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Statistical complexity measures are used to quantify the degree of complexity of the delayed logistic map, with linear and nonlinear feedback. We employ two methods for calculating the complexity measures, one with the 'histogram-based' probability distribution function and the other one with ordinal patterns. We show that these methods provide complementary information about the complexity of the delay-induced dynamics: there are parameter regions where the histogram-based complexity is zero while the ordinal pattern complexity is not, and vice versa. We also show that the time series generated from the nonlinear delayed logistic map can present zero missing or forbidden patterns, i.e. all possible ordinal patterns are realized into orbits.

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Detecting dynamical changes in time series using the permutation entropy

Cited by 471 publications

References 37 publications

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Trend forecasting based on Singular Spectrum Analysis of traffic workload in a large-scale wireless LAN

Quantifying the complexity of the delayed logistic map

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