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Examinando por Materia "ENTROPIA"

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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."
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."
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"
Characterization of electric load with information theory quantifiers
(2017-01) Aquino, Andre L. L.; Ramos, Heitor S.; Frery, Alejandro C.; Viana, Leonardo P.; Cavalcante, Tamer S. G.; Rosso, Osvaldo A.
"This paper presents a study of the electric load behavior based on the Causality Complexity–Entropy Plane.We use a public data set, namely REDD, which contains detailed power usage information from several domestic appliances. In our characterization, we use the available power data of the circuit/devices of all houses. The Bandt–Pompe methodology combined with the Causality Complexity–Entropy Plane was used to identify and characterize regimes and behaviors over these data. The results showed that this characterization provides a useful insight into the underlying dynamics that govern the electric load."
Clasificación de Imágenes SAR utilizando descriptores de textura
(2021-10) Gambini, Juliana; Rey, Andrea; Delrieux, Claudio
"Las imágenes SAR (Sythetic Aperture Radar) y PolSAR (Polarimetric Synthetic Aperture Radar) cumplen un rol fundamental en el monitoreo ambiental y observación terrestre debido a que proveen información que las imágenes ópticas no proporcionan. Sin embargo, estas imágenes están contaminadas con un ruido inherente al méetodo de captura llamado ruido speckle que dificulta su análisis e interpretación automática. Los modelos avanzados de segmentación de imágenes SAR están dedicados a resolver las dificultades que este ruido provoca. En este sentido, resulta de suma importancia el estudio de parámetros que describan las características estructurales de textura de imagen en presencia de ruido speckle y permitan su interpretación automática. En este trabajo, se propone un nuevo modelo de clasificación de imágenes SAR basado en el cálculo de descriptores de textura locales, formando un vector característico, el cual involucra estimaciones de parámetros de una distribución de probabilidad, estimaciones de la dimensión fractal y entropía de Tsallis. Luego, el etiquetado de cada pixel se realiza utilizando el método de clasificación supervisada SVM (Support Vector Machine). Se analizan los resultados de aplicar el algoritmo propuesto en imágenes SAR sintéticas, simples y con valores extremos agregados, los cuales resultan altamente prometedores para aplicarse en imágenes reales."
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."
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."
Diferencias en las aplicaciones de los mapas de entropía de Shannon-entropía de permutación para acciones de mercados de valores de países desarrollados y países en vía de desarrollo
(2014) Micheloni, Matías; Miranda, Lucía; Redelico, Francisco
"Tomando conceptos de los estudios de Zunino et al, se definió un nuevo análisis de las aplicaciones de la Entropía de Permutación aplicada, en este nuevo enfoque, a acciones de mercados de valores, en lugar de índices. El objetivo de este estudio consistió en validar los tres grupos de países propuestos por Zunino et al en su trabajo en base a las series de tiempos de los índices de mercados de valores, promediando los resultados obtenidos de las acciones analizadas en cada país. Luego, se buscaron las diferencias que existen en estas agrupaciones cuando se separan las acciones según la industria a la que pertenece la empresa, lo que resultó en algunos casos muy similar (Oil&Gas y Retail, por ejemplo) y en algunos se requirió cambiar los parámetros del análisis para interpretar los resultados (Power&Energy y Telco, por ejemplo). Finalmente, se realizó un estudio de correlación entre las Entropías de Shannon y de Permutación para hallar una posible interpretación financiera de éstos parámetros. De todos modos, los coeficientes de correlación no resultaron ser los suficientemente grandes como para afirmar que exista una correlación entre ambas, aunque es necesario destacar que éste último análisis mostró una correlación sensiblemente mayor entre el riesgo y la Entropía de Permutación para los países en vías de desarrollo que para los países desarrollados."
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. "
Libor at crossroads: stochastic switching detection using information theory quantifiers
(2016-07) Fernández Bariviera, Aurelio; Guercio, M. Belén; Martinez, Lisana B.; Rosso, Osvaldo A.
"This paper studies the 28 time series of Libor rates, classified in seven maturities and four currencies, during the last 14 years. The analysis was performed using a novel technique in financial economics: the Complexity-Entropy Causality Plane. This planar representation allows the discrimination of different stochastic and chaotic regimes. Using a temporal analysis based on moving windows, this paper unveils an abnormal movement of Libor time series around the period of the 2007 financial crisis. This alteration in the stochastic dynamics of Libor is contemporary of what press called "Libor scandal", i.e. the manipulation of interest rates carried out by several prime banks. We argue that our methodology is suitable as a market watch mechanism, as it makes visible the temporal redution in informational efficiency of the market."
Permutation entropy based time series analysis: equalities in the input signal can lead to false conclusions
(2017-06) Zunino, Luciano; Olivares, Felipe; Scholkmann, Felix; Rosso, Osvaldo A.
"A symbolic encoding scheme, based on the ordinal relation between the amplitude of neighboring values of a given data sequence, should be implemented before estimating the permutation entropy. Consequently, equalities in the analyzed signal, i.e. repeated equal values, deserve special attention and treatment. In this work, we carefully study the effect that the presence of equalities has on permutation entropy estimated values when these ties are symbolized, as it is commonly done, according to their order of appearance. On the one hand, the analysis of computer-generated time series is initially developed to understand the incidence of repeated values on permutation entropy estimations in controlled scenarios. The presence of temporal correlations is erroneously concluded when true pseudorandom time series with low amplitude resolutions are considered. On the other hand, the analysis of real-world data is included to illustrate how the presence of a significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts."
Permutation min-entropy: an improved quantifier for unveiling subtle temporal correlations
(2015-01) Zunino, Luciano; Olivares, Felipe; Rosso, Osvaldo A.
"The aim of this letter is to introduce the permutation min-entropy as an improved symbolic tool for identifying the existence of hidden temporal correlations in time series. On the one hand, analytical results obtained for the fractional Brownian motion stochastic model theoretically support this hypothesis. On the other hand, the analysis of several computer-generated and experimentally observed time series illustrate that the proposed symbolic quantifier is a versatile and practical tool for identifying the presence of subtle temporal structures in complex dynamical systems. Comparisons against the results obtained with other tools confirm its usefulness as an alternative and/or complementary measure of temporal correlations."
Propiedades del estimador de la entropía de permutación y su aplicación en problemas de ingeniería
(2018) Traversaro Varela, Francisco; Redelico, Francisco; Risk, Marcelo
"Las medidas provenientes de la Teoría de la Información evaluadas en una distribución de probabilidades adecuada son una herramienta muy potente para caracterizar la complejidad de un sistema dinámico. En particular se estudia la Entropía Informacional de Shannon evaluada en la Función de Distribución de Bandt y Pompe: la Entropía de Permutación. Esta medida es de cómputo rápido, no requiere pre-procesamiento de la señal, contiene información de la estructura de autocorrelaciones de la serie de tiempo y se basa sobre un supuesto de estacionariedad muy débil. La Entropía de Permutación ha sido ampliamente utilizada en aplicaciones de Ingeniería. Sin embrago, hasta el momento el estudio de las propiedades estadísticas de su estimación ha sido poco desarrollado, relegando su utilización a estudios descriptivos de los sistemas dinámicos bajo estudio. En esta Tesis, en una primera instancia se hace un estudio exhaustivo de la Función de Distribución de Bandt Pompe y las distintas metodologías utilizadas para calcularla. Luego se estudian las medidas de complejidad evaluadas en esta distribución y su comportamiento, y se resuelven problemáticas particulares: la aplicación de la Entropía de Permutación a series de tiempo con valores repetidos y la influencia de la distribución marginal de los datos en la estimación de la misma. Finalmente esta Tesis propone una metodología estadística computacional, el bootstrap paramétrico, para obtener una aproximación de la distribución del estimador de la Entropía de Permutación y de esta manera poder hacer inferencia con esta medida de complejidad. Esto permite la construcción de un test estadístico que puede detectar cambios en la dinámica de un proceso mediante la Entropía de Permutación. Se hace un estudio final acerca de la influencia del ruido observacional en la estimación de la Entropía de Permutación mediante el uso de los tests estadísticos propuestos."
Speckle noise reduction in SAR images using information theory
(2020) Chan, Debora; Gambini, Juliana; Frery, Alejandro C.
"In this work, a new nonlocal means filter for single-look speckled data using the Shannon and Renyi entropies is proposed. The measure of similarity between a central window and patches of the image is based on a statistical test for comparing if two samples have the same entropy and hence have the same distribution. The results are encouraging, as the filtered image has better signal-to-noise ratio, it preserves the mean, and the edges are not severely blurred."
Statistical properties of the entropy from ordinal patterns
(2022) Chagas, Eduarda T. C.; Frery, Alejandro C.; Gambini, Juliana; Lucini, María M.; Ramos, Heitor S.; Rey, Andrea
"The ultimate purpose of the statistical analysis of ordinal patterns is to characterize the distribution of the features they induce. In particular, knowing the joint distribution of the pair entropy-statistical complexity for a large class of time series models would allow statistical tests that are unavailable to date. Working in this direction, we characterize the asymptotic distribution of the empirical Shannon’s entropy for any model under which the true normalized entropy is neither zero nor one. We obtain the asymptotic distribution from the central limit theorem (assuming large time series), the multivariate delta method, and a third-order correction of its mean value. We discuss the applicability of other results (exact, first-, and second-order corrections) regarding their accuracy and numerical stability. Within a general framework for building test statistics about Shannon’s entropy, we present a bilateral test that verifies if there is enough evidence to reject the hypothesis that two signals produce ordinal patterns with the same Shannon’s entropy. We applied this bilateral test to the daily maximum temperature time series from three cities (Dublin, Edinburgh, and Miami) and obtained sensible results."
Topotecan penetration assessment in retinoblastoma cells using Shannon entropy and coefficient of variation
(2019-09) Howlin, Marcelo; Chan, Debora; Ramele, Rodrigo; Gambini, Juliana
"Retinoblastoma is a common intraocular tumor of childhood. One of the medications used as an antineoplastic agent for retinoblastoma treatment is topotecan. Its penetration into living tumorspheres is quantified using confocal microscopy. Topotecan is a fluorescent drug and it dyes the living tissue. Then, it is recorded in a sequence of images over a period of time. The effective penetration of the drug depends on culture characteristics and requires a very specific timing which is calculated empirically by an expert. The purpose of this work is to offer a model to automatically estimate and evaluate the penetration time of topotecan in a cell, based on the information obtained from a sequence of tumorsphere images and using Shannon entropy and coefficient of variation."

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