Comparing different approaches to compute Permutation Entropy with coarse time series

Traversaro, Francisco; Ciarrocchi, Nicolas; Pollo-Cattaneo, María Florencia; Redelico, Francisco O.

doi:10.1016/j.physa.2018.08.021

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…Other studies focused on the drawback of possible ambiguities due to equal values in time series for PermEn. 142,[159][160][161][162][163] For the biomedical field, Bandt proposed -in 2017 -a new version of PermEn that he interpreted as distance to white noise. 164 In the latter work, distance to white noise was used as a parameter to measure depth of sleep.…”

Section: Ws-rv961x669mentioning

confidence: 99%

Entropy Analysis of Univariate Biomedical Signals: Review and Comparison of Methods

Azami

Faes

Escudero

et al. 2022

Contemporary Mathematics and Its Applications: Monographs, Expositions and Lecture Notes

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Nonlinear techniques have found an increasing interest in the dynamical analysis of various kinds of systems. Among these techniques, entropy-based metrics have emerged as practical alternatives to classical techniques due to their wide applicability in different scenarios, specially to short and noisy processes. Issued from information theory, entropy approaches are of great interest to evaluate the degree of irregularity and complexity of physical, physiological, social, and econometric systems. Based on Shannon entropy and conditional entropy (CE), various techniques have been proposed; among them, approximate entropy, sample entropy, fuzzy entropy, distribution entropy, permutation entropy, and dispersion entropy are probably the most well-known. After a presentation of the basic information-theoretic functionals, these measures are detailed, together with recent proposals inspired by nearest neighbors and parametric approaches. Moreover, the role of dimension, data length, and parameters in using these measures

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Section: Ws-rv961x669mentioning

confidence: 99%

Entropy Analysis of Univariate Biomedical Signals: Review and Comparison of Methods

Azami

Faes

Escudero

et al. 2022

Contemporary Mathematics and Its Applications: Monographs, Expositions and Lecture Notes

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“…In this paper, permutation entropy (PE) algorithm is introduced into IMF component processing. 38 The short-term wind speed data processed by EEMD are analyzed by PE algorithm. The components with similar PE values are merged into one.…”

Section: Introduction Of the Proposed Approachmentioning

confidence: 99%

“…However, the excessive number of IMFs obtained from short‐term wind speed data decomposed by EEMD will lead to long training time, which is not suitable for short‐term wind speed online prediction. In this paper, permutation entropy (PE) algorithm is introduced into IMF component processing . The short‐term wind speed data processed by EEMD are analyzed by PE algorithm.…”

Section: Introductionmentioning

confidence: 99%

A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

Tian

Wang

2019

Wind Energy

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Accurate prediction of short-term wind speed is of great significance to the operation and maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable operation of power grids. A prediction approach for short-term wind speed using ensemble empirical mode decomposition-permutation entropy and regularized extreme learning machine is proposed. Firstly, wind speed time series is decomposed into several components with different frequency by ensemble empirical mode decomposition, which can reduce the non-stationarity of the original time series. The permutation entropy value for each component is used to analyze its complexity. The components can be recombined to obtain a set of new subsequences. Then, different prediction models based on regularized extreme learning machine are used to predict each subsequence. Fivefold cross validation is used to improve the reliability of the regularized extreme learning machine model. Finally, the predicted value of each subsequence is superimposed to obtain the final predictive result. Ten minutes, 30 minutes, and 1 hour short-term wind speed data from wind farms in Liaoning Province, China, are used for conducting experiments. The experimental results indicate that the values of the root mean square error of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.5629, 0.4473, and 0.5697; mean absolute error are 0.4427, 3.0701, and 0.4897; mean absolute percentile error are 4.1456%, 16.8166%, and 6.8166%; relative root mean square are 0.0505, 0.2997, and 0.2609; square sum error are 55.5263, 59.6347, and 64.9154; and the Theil inequality coefficient are 0.0235, 0.0808, and 0.0625, which are much lower than those of the comparison methods. The values of the R square of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.9363, 0.9161, and 0.9472, and the index of agreement are 0.9994, 0.9925, and 0.9894, whichare higher than those of the comparison methods. The Pearson's test results show that the association strength between the actual value and the predicted values of the proposed approach is stronger. Also, the proposed prediction approach in this paper has higher reliability under the same confidence level. The effectiveness of the proposed prediction approach for short-term wind speed is verified.

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“…In this regard, Permutation Entropy (PE) and related transformations using symbol dynamics [ 15 ] have presented a meta-analytical tool, as they can be applied to the datasets independent of the physical/physiological system parameters, and a priori knowledge of the system behavior is not necessary [ 16 ]. PE was introduced in 1982 by Bandt & Pompe [ 17 ], and since then has gained a reputation in many fields of science with numerous applications and also further developments of the basic method [ 18 , 19 ]. In essence, PE is based on the calculation of the partial fulfilment of the total possible system realizations in phase space by employing Shannon Entropy [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Autonomic and Central Nervous System Activity by Permutation Entropy during Short Sojourn in Antarctica

Cotuk

Duru

Aktaş

2019

Entropy

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The aim of this study was to monitor acute response patterns of autonomic and central nervous system activity during an encounter with Antarctica by synchronously recording heart rate variability (HRV) and electroencephalography (EEG). On three different time-points during the two-week sea journey, the EEG and HRV were recorded from nine male scientists who participated in “The First Turkish Antarctic Research Expedition”. The recordings were performed in a relaxed state with the eyes open, eyes closed, and during a space quantity perception test. For the EEG recordings, the wireless 14 channel EPOC-Emotiv device was used, and for the HRV recordings, a Polar heart rate monitor S810i was used. The HRV data were analyzed by time/frequency domain parameters and ordinal pattern statistics. For the EEG data, spectral band power in the conventional frequency bands, as well as permutation entropy values were calculated. Regarding HRV, neither conventional nor permutation entropy calculations produced significant differences for the different journey time-points, but only permutation entropy was able to differentiate between the testing conditions. During the cognitive test, permutation entropy values increased significantly, whereas the conventional HRV parameters did not show any significant differences. In the EEG analysis, the ordinal pattern statistics revealed significant transitions in the course of the sea voyage as permutation entropy values decreased, whereas spectral band power analysis could not detect any significant difference. Permutation entropy analysis was further able to differentiate between the three testing conditions as well between the brain regions. In the conventional spectral band power analysis, alpha band power could separate the three testing conditions and brain regions, and beta band power could only do so for the brain regions. This superiority of permutation entropy in discerning subtle differences in the autonomic and central nervous system’s responses to an overwhelming subjective experience renders it suitable as an analysis tool for biomonitoring in extreme environments.

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Comparing different approaches to compute Permutation Entropy with coarse time series

Cited by 8 publications

References 26 publications

Entropy Analysis of Univariate Biomedical Signals: Review and Comparison of Methods

Entropy Analysis of Univariate Biomedical Signals: Review and Comparison of Methods

A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

Monitoring Autonomic and Central Nervous System Activity by Permutation Entropy during Short Sojourn in Antarctica

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