Examinando por Autor "Redelico, Francisco"
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Artículo de Publicación Periódica Analysis of ischaemic crisis using the informational causal entropy-complexity plane(2018-07) Legnani, Walter; Traversaro Varela, Francisco; Redelico, Francisco; Cymberknop, Leandro J.; Armentano, Ricardo L.; Rosso, Osvaldo A."In the present work, an ischaemic process, mainly focused on the reperfusion stage, is studied using the informational causal entropy-complexity plane. Ischaemic wall behavior under this condition was analyzed through wall thickness and ventricular pressure variations, acquired during an obstructive flow maneuver performed on left coronary arteries of surgically instrumented animals. Basically, the induction of ischaemia depends on the temporary occlusion of left circumflex coronary artery (which supplies blood to the posterior left ventricular wall) that lasts for a few seconds. Normal perfusion of the wall was then reestablished while the anterior ventricular wall remained adequately perfused during the entire maneuver. The obtained results showed that system dynamics could be effectively described by entropy-complexity loops, in both abnormally and well perfused walls. These results could contribute to making an objective indicator of the recovery heart tissues after an ischaemic process, in a way to quantify the restoration of myocardial behavior after the supply of oxygen to the ventricular wall was suppressed for a brief period."Artículo de Publicación Periódica Bandt-Pompe symbolization dynamics for time series with tied values: a data-driven approach(2018-07) Traversaro Varela, Francisco; Redelico, Francisco; Risk, Marcelo; Frery, Alejandro C.; Rosso, Osvaldo A."In 2002, Bandt and Pompe [Phys. Rev. Lett. 88, 174102 (2002)] introduced a successfully symbolic encoding scheme based on the ordinal relation between the amplitude of neighboring values of a given data sequence, from which the permutation entropy can be evaluated. Equalities in the analyzed sequence, for example, repeated equal values, deserve special attention and treatment as was shown recently by Zunino and co-workers [Phys. Lett. A 381, 1883 (2017)]. A significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts. In the present contribution, we review the different existing methodologies for treating time series with tied values by classifying them according to their different strategies. In addition, a novel data-driven imputation is presented that proves to outperform the existing methodologies and avoid the false conclusions pointed by Zunino and co-workers."Artículo de Publicación Periódica Characterization of autoregressive processes using entropic quantifiers(2018-01) Traversaro Varela, Francisco; Redelico, Francisco"The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets"Artículo de Publicación Periódica Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier(2017-02) Redelico, Francisco; Traversaro Varela, Francisco; García, María del Carmen; Silva, Walter; Rosso, Osvaldo A.; Risk, Marcelo"In this contribution, a comparison between different permutation entropies as classifiers of electroencephalogram (EEG) records corresponding to normal and pre-ictal states is made. A discrete probability distribution function derived from symbolization techniques applied to the EEG signal is used to calculate the Tsallis entropy, Shannon Entropy, Renyi Entropy, and Min Entropy, and they are used separately as the only independent variable in a logistic regression model in order to evaluate its capacity as a classification variable in a inferential manner. The area under the Receiver Operating Characteristic (ROC) curve, along with the accuracy, sensitivity, and specificity are used to compare the models. All the permutation entropies are excellent classifiers, with an accuracy greater than 94.5% in every case, and a sensitivity greater than 97%. Accounting for the amplitude in the symbolization technique retains more information of the signal than its counterparts, and it could be a good candidate for automatic classification of EEG signals."Artículo de Publicación Periódica Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy(2018-04) Traversaro Varela, Francisco; Redelico, Francisco"In nonlinear dynamics, and to a lesser extent in other fields, a widely used measure of complexity is the Permutation Entropy. But there is still no known method to determine the accuracy of this measure. There has been little research on the statistical properties of this quantity that characterize time series. The literature describes some resampling methods of quantities used in nonlinear dynamics - as the largest Lyapunov exponent - but these seems to fail. In this contribution, we propose a parametric bootstrap methodology using a symbolic representation of the time series to obtain the distribution of the Permutation Entropy estimator. We perform several time series simulations given by well-known stochastic processes: the 1/f α noise family, and show in each case that the proposed accuracy measure is as efficient as the one obtained by the frequentist approach of repeating the experiment. The complexity of brain electrical activity, measured by the Permutation Entropy, has been extensively used in epilepsy research for detection in dynamical changes in electroencephalogram (EEG) signal with no consideration of the variability of this complexity measure. An application of the parametric bootstrap methodology is used to compare normal and pre-ictal EEG signals."Artículo de Publicación Periódica Evaluation of the status of rotary machines by time causal information theory quantifiers(2017-03) Redelico, Francisco; Traversaro Varela, Francisco; Oyarzábal, Nicolás Andrés; Vilaboa, Iván; Rosso, Osvaldo A."In this paper several causal Information Theory quantifiers, i.e. Shannon entropy, statistical complexity and Fisher information using the Bandt and Pompe permutation probability distribution, measure are applied to describe the behavior of a rotating machine. An experiment was conducted where a rotating machine runs balanced and then, after a misalignment, runs unbalanced. All the causal Information Theory quantifiers applied are capable to distinguish between both states and grasp the corresponding transition between them. "