Heart rate variability analysis can be used for a diagnosis of the cardiac diseases. The HRV analysis methods are divided into linear and nonlinear methods. Time-domain method is one of the simplest method and belongs to linear methods. Detrended fluctuation analysis DFA is nonlinear method made relatively recently. In this paper, it has been done the comparison of these two methods based on the changes detection in HRV caused by an ischemia.

Accelerometer data can be analyzed using a variety of methods which are effective in the clinical setting. Time-series analysis is used to analyze spatiotemporal variables in various populations. More recently, investigators have focused on gait complexity and the structure of spatiotemporal variations during walking and running. This study evaluated the use of time-series analyses to determine gait parameters during running. Subjects were college-age female soccer players. Accelerometer data were collected using GPS-embedded trunk-mounted accelerometers. Customized Matlab® programs were developed that included Gaussian continuous wavelet transform (CWT) to determine spatiotemporal characteristics, detrended fluctuation analysis (DFA) to examine gait complexity and autocorrelation analyses (ACF) to assess gait regularity. Reliability was examined using repeated running efforts and intraclass correlation. Proof of concept was determined by examining differences in each variable between various running speeds. Applicability was established by examining gait before and after fatiguing activity. The results showed most variables had excellent reliability. Test-retest R2 values for these variables ranged from 0.8 to 1.0. Low reliability was seen in bilateral comparisons of gait symmetry. Increases in running speed resulted in expected changes in spatiotemporal and acceleration variables. Fatiguing exercise had minimal effects on spatiotemporal variables but resulted in noticeable declines in complexity. This investigation shows that GPS-embedded trunk-mounted accelerometers can be effectively used to assess running gait. CWT and DFA yield reliable measures of spatiotemporal characteristics of gait and gait complexity. The effects of running speed and fatigue on these variables provides proof of concepts and applicability for this analytical approach. / Master of Science / Fitness trackers have become widely accessible and easy to use. So much so that athletic teams have been using them to track activity throughout the season. Researchers are able to manipulate data generated from the fitness monitors to assess many different variables including gait. Monitoring gait may generate important information about the condition of the individual. As a person fatigues, running form is theorized to breakdown, which increases injury risk. Therefore the ability to monitor gait may be advantageous in preventing injury. The purpose of this study is to show that the methods in this study are reproducible, respond reasonably to changes in speed, and to observe the changes of gait in the presence of fatigue or on tired legs. Three analyses are used in this study. The first method called autocorrelation, overlays acceleration signals of consecutive foot strikes, and determines the similarity between them. The second method utilizes a wave transformation technique that is able to determine foot contact times. The final method attempts to determine any pattern in the running stride. This method looks for changes in the structure of the pattern. Less structure would indicate a stride that is fatigued. The results showed that the methods of gait analysis used in this study were reproducible and responded appropriately with changes in speed. Small changes in gait were observed due to the presence of fatigue. Further investigation into the use of these methods to determine changes in gait due to the presence of fatigue are warranted.

Recently there have been reports that certain physiological data seem to have the properties of long-range correlation and self-similarity. These two properties can be characterized by a long-range dependent parameter d, as well as a self-similar parameter H. In Peng et al (1995), the alteration of long-range correlations with life-threatening pathologies are studied by analyzing the heart rate data of different groups of subjects. The self-similarity properties of two well-known processes, namely the Fractional Brownian Motion (FBM) and the Fractional ARIMA (FARIMA), are of interest to see if it is suitable to be used to model the heart rate data in order to examine the health conditions of some patients. The Embedded Branching Process (EBP) method for estimating parameter $H$ and a goodness of fit test for examining the self-similarity of a process based on the EBP method are proposed in Jones and Shen (2004). In this work, the performance of the goodness of fit test are examined using simulated data from the FBM and FARIMA processes. A modification of the distribution of the test statistics under null hypothesis is proposed and has been modified to be more appropriate. Some simulation comparisons of different estimation methods of the parameter $H$ for some FARIMA processes are also presented and applied to heart rate data obtained from Kaohsiung Veterans General Hospital.

No description available.

Les modèles de mutations décrivent le processus d’apparitions rares et aléatoires de mutations au cours de lacroissance d’une population de cellules. Les échantillons obtenus sont constitués de nombres finaux de cellules mutantes,qui peuvent être couplés avec des nombres totaux de cellules ou un nombre moyen de cellules en fin d’expérience.La loi du nombre final de mutantes est une loi à queue lourde : de grands décomptes, appelés “jackpots”,apparaissent fréquemment dans les données.Une construction générale des modèles se décompose en troisniveaux. Le premier niveau est l’apparition de mutations aléatoires au cours d’un processus de croissance de population.En pratique, les divisions cellulaires sont très nombreuses, et la probabilité qu’une de ces divisions conduise à une mutation est faible,ce qui justifie une approximation poissonnienne pour le nombre de mutations survenant pendant un temps d’observation donné.Le second niveau est celui des durées de développement des clones issus de cellules mutantes. Du fait de la croissance exponentielle,la majeure partie des mutations ont lieu à la fin du processus, et les durées de développement sont alors indépendanteset exponentiellement distribuées. Le troisième niveau concerne le nombre decellules qu’un clone issu d’une cellule mutante atteint pendant une durée de développement donnée.La loi de ce nombre dépend principalement de la loi des instants de division des mutantes.Le modèle classique, dit de Luria-Delbrück, suppose que les développements cellulaires des cellules normales aussi bien que mutantess’effectue selon un processus de Yule. On peut dans ce cas calculer expliciter la loi du nombre final de mutantes.Elle dépend de deux paramètres, qui sont le nombre moyen de mutations et le paramètre de fitness (ratio des taux de croissance des deux types de cellules).Le problème statistique consiste à estimer ces deux paramètres au vu d’un échantillon denombres finaux de mutantes. Il peut être résolu par maximisation de la vraisemblance,ou bien par une méthode basée sur la fonction génératrice. Diviser l'estimation du nombre moyen de mutations par le nombre total de cellulespermet alors d'estimer la probabilité d’apparition d’une mutation au cours d’une division cellulaire.L’estimation de cette probabilité est d’une importancecruciale dans plusieurs domaines de la médecine et debiologie: rechute de cancer, résistance aux antibiotiques de Mycobacterium Tuberculosis, etc.La difficulté provient de ce que les hypothèses de modélisation sous lesquelles la distribution du nombre final de mutants est explicitesont irréalistes.Or estimer les paramètres d’un modèle quand la réalité en suit un autre conduit nécessairement à un biais d’estimation.Il est donc nécessaire de disposer de méthodes d’estimation robustes pour lesquelles le biais, en particulier sur la probabilité de mutation,reste le moins sensible possible aux hypothèses de modélisation.Cette thèse contient une étude probabiliste et statistique de modèles de mutations prenant en compte les sources de biais suivantes : durées de vie non exponentielles, morts cellulaires,variabilité du nombre final de cellules, durées de vie non-exponentielles et non-identiquement distribuées, dilution de la population initiale.Des études par simulation des méthodes considérées sont effectuées afin de proposer, selon les caractéristiques du modèle,l’estimation la plus fiable possible. Ces méthodes ont également été appliquées à desjeux de données réelles, afin de comparer les résultats avec les estimations obtenues avec les modèles classiques.Un package R a été implémenté en collaboration avec Rémy Drouilhet et Stéphane Despréaux et est disponible sur le CRAN.Ce package est constitué des différents résultats obtenus au cours de ce travail. Il contient des fonctions dédiées aux modèles de mutations,ainsi qu'à l'estimation des paramètres. Les applications ont été développées pour le Labex TOUCAN (Toulouse Cancer). / Mutation models are probabilistic descriptions of the growth of a population of cells, where mutationsoccur randomly during the process. Data are samples of integers, interpreted as final numbers ofmutant cells. These numbers may be coupled with final numbers of cells (mutant and non mutant) or a mean final number of cells.The frequent appearance in the data of very large mutant counts, usually called “jackpots”, evidencesheavy-tailed probability distributions.Any mutation model can be interpreted as the result of three ingredients. The first ingredient is about the number of mutations occuring with small probabilityamong a large number of cell divisions. Due to the law of small numbers, the number of mutations approximately follows aPoisson distribution. The second ingredient models the developing duration of the clone stemming from each mutation. Due to exponentialgrowth, most mutations occur close to the end of the experiment. Thus the developing time of arandom clone has exponential distribution. The last ingredients represents the number of mutant cells that any clone developing for a given time will produce. Thedistribution of this number depends mainly on the distribution of division times of mutants.One of the most used mutation model is the Luria-Delbrück model.In these model, division times of mutant cells were supposed to be exponentially distributed.Thus a clone develops according to a Yule process and its size at any given time follows a geometric distribution.This approach leads to a family of probability distributions which depend on the expected number of mutations and the relative fitness, which is the ratio between the growth rate of normal cells to that of mutants.The statistic purpose of these models is the estimation of these parameters. The probability for amutant cell to appear upon any given cell division is estimated dividing the mean number of mutations by the mean final number of cells.Given samples of final mutant counts, it is possible to build estimators maximizing the likelihood, or usingprobability generating function.Computing robust estimates is of crucial importance in medical applications, like cancer tumor relapse or multidrug resistance of Mycobacterium Tuberculosis for instance.The problem with classical mutation models, is that they are based on quite unrealistic assumptions: constant final number of cells,no cell deaths, exponential distribution of lifetimes, or time homogeneity. Using a model for estimation, when thedata have been generated by another one, necessarily induces a bias on estimates.Several sources of bias has been partially dealed until now: non-exponential lifetimes, cell deaths, fluctuations of the final count of cells,dependence of the lifetimes, plating efficiency. The time homogeneity remains untreated.This thesis contains probabilistic and statistic study of mutation models taking into account the following bias sources:non-exponential and non-identical lifetimes, cell deaths, fluctuations of the final count of cells, plating efficiency.Simulation studies has been performed in order to propose robust estimation methods, whatever the modeling assumptions.The methods have also been applied to real data sets, to compare the results with the estimates obtained under classical models.An R package based on the different results obtained in this work has been implemented (joint work with Rémy Drouilhetand Stéphane Despréaux) and is available on the CRAN. It includes functions dedicated to the mutation models and parameter estimation.The applications have been developed for the Labex TOUCAN (Toulouse Cancer).

Submitted by (ana.araujo@ufrpe.br) on 2016-05-25T14:39:16Z No. of bitstreams: 1 Barbara Camboim Lopes de Figueiredo.pdf: 2032958 bytes, checksum: d463c6ab534a96f1ce5aac33c2dde210 (MD5) / Made available in DSpace on 2016-05-25T14:39:16Z (GMT). No. of bitstreams: 1 Barbara Camboim Lopes de Figueiredo.pdf: 2032958 bytes, checksum: d463c6ab534a96f1ce5aac33c2dde210 (MD5) Previous issue date: 2014-02-24 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The study of climate has great importance, given that a variation of climatic elements affect the economy of a certain region and life of the inhabitants. Climate variables temperature, humidity, atmospheric pressure, solar radiation, precipitation and wind can be affected by geophysical and environmental factors such as latitude, altitude, air mass, proximity to sea, sea currents and vegetation. Wind is the most complex climate element representing the natural phenomenon of turbulence, it is characterized by high temporal and spatial variability. Wind is generated by atmospheric air mass movement, and has influence on various environmental phenomena such as soil erosion, pollutant dispersal and transport of pollen and seeds. Knowing wind speed temporal and spatial distribution is crucial to evaluate the potential for generation of eolic energy. In this work we study long-term correlations in wind speed temporal series registered at twelve meteorological stations in the state of Pernambuco, Brazil. To this end we apply Multifractal Detrended Fluctuation Analysis (MF-DFA) on hourly wind speed data for the period 2008-2011. All the analyzed series exhibit multifractal properties with generalized Hurst exponents above 0.5 indicating persistent temporal dynamics for both, small and large fluctuations. We also calculate other multifractal measures Rényi exponent and singularity spectrum, and complexity parameters, position of maximum, width and asymmetry of multifractral spectrum. No correlation was detected between complexity parameters and the geographic parameters longitude, latitude and altitude of the station, except for asymmetry of multifractal spectrum: negative correlation with longitude for maximum wind speed and negative correlation with latitude for average wind speed. However for all stations the strength of multifractality (indicated by width of multifractal spectrum) is greater for maximum wind speed then for average wind speed. These results contribute to a better understanding of the nature of stochastic processes governing wind dynamics which is necessary for development of more accurate predictive models for wind speed temporal variability and diverse phenomena influenced by wind. / O estudo do clima tem grande importância visto que a variação em elementos climáticos afeta a economia de uma região e a vida das pessoas que ali habitam. As variáveis climáticas temperatura, umidade, pressão atmosférica, radiação solar, precipitação e vento podem ser influenciadas por diversos fatores, geofísicos e ambientais, tais como latitude, altitude, massas de ar, continentalidade e maritmidade, relevo e vegetação. Um dos mais complexos elementos do clima é o vento, pelo fato de representar um fenômeno natural de turbulência, caracterizado por uma grande variabilidade temporal e espacial. O vento é gerado pelo movimento das massas de ar e pode influenciar vários fenômenos ambientais como erosão do solo, dispersão de poluentes e transporte de pólen e sementes. O conhecimento da distribuição temporal e espacial da velocidade do vento é crucial para avaliação do potencial eólico de uma região. Neste trabalho estudaram-se correlações de longo alcance das séries temporais de velocidade do vento registradas em 12 estações meteorológicas durante o período de 2008 a 2011 no estado de Pernambuco aplicando-se o método Multifractal Detrended Fluctuation Analysis (MF-DFA) nas séries temporais horárias. Todas as séries analisadas mostram as propriedades multifractais com valores de expoente generalizado de Hurst acima de 0,5 indicando uma dinâmica persistente para pequenas e grande flutuações. Foram calculadas também as outras medidas multifractais, o expoente Rényi e o espectro multifractal bem como os parâmetros de complexidade: posição do máximo, largura e assimetria do espectro multifractal. Não foram encontradas correlação entre os parâmetros de complexidade e as coordenadas geográficas: longitude, latitude e altitude, exceto a medida de assimetria do espectro multifractal: correlação negativa entre a rajada e longitude e entre velocidade e latitude. Para todas estações as larguras do espectro multifractal foram maiores para a rajada que para a velocidade, indicando uma multifractalidade mais forte. Estes resultados contribuem para uma melhor compreensão da natureza dos processos estocásticos geradores da dinâmica do vento, necessária para o desenvolvimento de modelos confiáveis para predição da variabilidade temporal do vento e dos diversos fenômenos influenciados pelo mesmo.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-05T15:38:42Z No. of bitstreams: 1 Erinaldo Leite Batista Almeida.pdf: 3620819 bytes, checksum: b2532ef7524f47d5417d01445fec797b (MD5) / Made available in DSpace on 2016-07-05T15:38:42Z (GMT). No. of bitstreams: 1 Erinaldo Leite Batista Almeida.pdf: 3620819 bytes, checksum: b2532ef7524f47d5417d01445fec797b (MD5) Previous issue date: 2009-08-10 / Financial markets are complex systems that contain large numbers of interacting units, including interactions among various units in the same market and interactions between units in different markets. Various methods of economics, statistics and econophysics have been developed to analyze financial temporal series (such as price returns, share volume, number of transactions), and serve to establish theoretical models for underlying stochastic processes. The availability of financial data on the internet and increasing computational power have enabled researchers to conduct a large number of empirical studies on financial markets. These studies have shown some universal properties: the risk function of price returns is scale invariant, with power-law behavior and similar value of exponent for different markets; the absolute values of returns (volatility) exhibit long-range power-law correlations. In this work, we use methods if econophysics to study the statistical properties of Brazilian financial markets. We analyze and compare scale properties of risk functions and correlations in temporal series of price returns of agricultural commodities and stocks of various companies traded at Bovespa. We analyze the daily prices of five commodities and twenty stocks traded in the period 2000-2008. For both commodities and stocks, the risk function of daily price returns shows powerlaw behavior with the exponent outside the Levy stable region. The values of exponents are higher for stocks than for commodities. We use Detrended Fluctuation Analysis (DFA) to study correlations in daily time series of absolute values of returns (volatility). This method was developed to quantify long range correlations in non-stationary temporal series.All analyzed series show persistent behavior, meaning that large (small) values are more likely to be followed with large (small) values. The value of the DFA exponent is higher for commodities than for stocks. We also use Detrended Cross Correlation Analysis (DCCA) to study cross-correlations between two series. The values of DCCA exponents are above 0.5 for all series, indicating the existence of long range cross-correlations. This means that each stock or commodity has long memory of its own previous values and of previous values of other stocks or commodities studied. These results are in agreement with results obtained for American financial markets. / Mercados financeiros são caracterizados por um grande número de unidades e interações complexas, incluindo as interações internas (entre diferentes elementos de um mercado) e fatores externos (influência de outros mercados). Vários métodos de economia, estatística e recentemente econofísica foram desenvolvidos para analisar as séries temporais de variáveis financeiras (retorno de preços de ações, mercadorias e taxas de cambio, índice de mercado, volume de negociação, etc.), com objetivo de estabelecer os modelos teóricos para processos estocásticos que estão em base desses fenômenos. A disponibilidade de dados financeiros de vários mercados e crescente poder computacional resultaram em um grande número de estudos empíricos cujos resultados mostraram algumas propriedades universais: a função risco de retornos de preços segue uma lei de potência com o valor de expoente similar para os vários mercados; os valores absolutos de retornos possuem correlações de longo alcance. Neste trabalho foram usados os métodos de econofísica para estudar as propriedades estatísticas do mercado financeiro brasileiro. Foram analisadas e comparadas as propriedades de escala de função risco e de correlações em séries temporais de retornos de preços de mercadorias agrícolas e preços de ações de várias empresas negociadas na Bolsa de Valores de São Paulo (BOVESPA). Foram analisados os preços diários de cinco mercadorias: açúcar, algodão, café, soja e boi, registrados em período 2000-2008. Para ações, analisamos as características seguintes: preços de abertura, fechamento, valores máximo e mínimo, volume e montante. Todas as séries são diárias, registradas no período de 2000-2008. São estudadas 20 empresas divididas em 4 grupos: bancos, energia, telecomunicações e siderurgia (5 empresas de cada grupo). Para todas as séries estudadas a função risco de retornos de preços segue uma lei de potência com os valores de expoente maiores para ações do que para mercadorias. As correlações são analisadas para os valores absolutos de retornos de preços (volatilidade). Foi usado o método Detrended Fluctuation Analysis (DFA), desenvolvido para quantificar as correlações de longo alcance em séries temporais não estacionárias. Todas as séries mostraram um comportamento persistente, significando que os valores grandes (pequenos) tem maior probabilidade de serem seguidos por valores grandes (pequenos). Os valores de expoente DFA são maiores para mercadorias do que para as ações. Foi utilizada uma generalização de DFA, Detrended Cross Correlation Analysis (DCCA) para analisar as correlações cruzadas entre duas séries. Os valores de expoente DCCA para todas as séries estudadas indicam a existência de correlações cruzadas de longo alcance significando que os valores de cada série possuem memória de longo alcance de seus valores anteriores e também de valores anteriores de outras série. Os resultados estão em acordo com os resultados obtidos para mercado americano.

Submitted by (ana.araujo@ufrpe.br) on 2016-08-02T15:32:53Z No. of bitstreams: 1 Luciano Rodrigues da Silva.pdf: 1477739 bytes, checksum: e1ea61981eacbff2c9319865f5504f91 (MD5) / Made available in DSpace on 2016-08-02T15:32:53Z (GMT). No. of bitstreams: 1 Luciano Rodrigues da Silva.pdf: 1477739 bytes, checksum: e1ea61981eacbff2c9319865f5504f91 (MD5) Previous issue date: 2009-10-20 / Vegetation fires represent a natural hazard with severe ecological, social, health and economic consequences. Every year fires burn millions of hectares of forest worldwide and their number have been increasing, principally because of the increase in population and combustion material. The preservation of the environment depends on global and regional policies and methods of prevention and suppression of fires. To establish these methods it is necessarily to know the profile of fires: spatial location, time of occurrence, burned area, why they occur, and how they initiate and propagate. Recently, various methods of Statistical Physics (including data analysis and computational models) have been applied to provide additional information about spatial and temporal distribution of fire sequences, which is crucial for assessing various consequences of burning, such as emissions of gasses and particulates to the atmosphere, loss of biodiversity, loss of wildlife habitat, soil erosion etc. Several satellite systems (with different capabilities in terms of spatial resolution, sensitivity, spectral bands, and times and frequency of overpasses) are currently available for monitoring different fire characteristics: dry areas that are susceptible to wild fire outbreak, actively flaming fires, burned area and smoke, and trace gas emissions. Hotspots are satellite image pixels with infrared intensity corresponding to burning vegetation. A hotspot may represent one fire, or be one of several hotspots representing a larger fire. Together with other satellite data, thenumber of hot-spots can be used to estimate the burned area. In this work we study the dynamics of hotspots using the Detrended Fluctuation Analysis (DFA) method, which serves to quantify correlations in non stationary time series. We analyze daily hotspot temporal series detected in Brazil by various satellites during the period 1998-2008. The results show the existence of power-law long-range correlations that represent an important property of the underlying stochastic process. This property, also found in climatic phenomena, should be incorporated in theoretical models and computer simulations of the fire dynamics. / Incêndios em vegetação é um tipo de desastre natural com conseqüências ambientais, sociais econômicas, etc. Todos os anos incêndios destroem milhões de hectares das florestas e aumentam em número como conseqüência de vários fatores, principalmente de crescimento populacional e acúmulo de material combustível. A preservação de meio ambiente depende das políticas protecionistas globais e regionais adequadas às características de cada região. Para estabelecer essas políticas de controle e prevenção é necessário conhecer o perfil dos incêndios florestais: onde, quando e porque ocorrem. Além das estatísticas de ocorrências de incêndios os métodos emergentes da Física Estatística incluindo análise de dados e modelos computacionais, providenciam as informações adicionais sobre a distribuição e agrupamento espaço-temporal dos incêndios, que são cruciais para o estudo de várias conseqüências de fogo, como emissão de gases e partículas em atmosfera, perda de biodiversidade, erosão de solo, etc. Vários satélites (com características diferentes em termos de resolução espacial, bandas espectrais, tempo e freqüência de escaneamento) são disponíveis para monitoramento das varias características de fogos: áreas de risco, incêndios atualmente ativos, área queimada, fumaça, emissão de poluentes etc. Focos de calor são pixels na imagem de satélite com intensidade infravermelha correspondente a vegetação queimada. Um foco pode representar uma queimada, parte de um incêndio maior ou outras fontes de calor como, por exemplo, a reflexão de luz da superfície de um lago. O número de focos junto com outras informações providenciadas pelos satélites podem ser usados para estimar a área queimada, para detecção e monitoramento dos incêndios florestais, estimação de risco de fogo, e para avaliação da influencia de outros fatores ambientais. Neste trabalho estudamos a dinâmica de focos de calor no Brasil usando o método Detrended Fluctuation Analysis (DFA), desenvolvido para quantificar as correlações em séries temporais não estacionárias. Analisamos séries temporais diárias de focos de calor detectados no Brasil pelo vários satélites, durante o período 1998-2008. Os resultados mostram a existência de correlações de longo alcance persistentes, que representa uma propriedade importante dos processos estocásticos geradores desse fenômeno. Esta propriedade, também presente em fenômenos climáticos deveria ser incorporada em modelos teóricos e simulações computacionais de dinâmica de incêndios.

Submitted by (ana.araujo@ufrpe.br) on 2016-08-12T14:04:00Z No. of bitstreams: 1 Rosangela Silveira do Nascimento.pdf: 1012353 bytes, checksum: b5f0d139481ab0bfdd6a1f689175c1f6 (MD5) / Made available in DSpace on 2016-08-12T14:04:00Z (GMT). No. of bitstreams: 1 Rosangela Silveira do Nascimento.pdf: 1012353 bytes, checksum: b5f0d139481ab0bfdd6a1f689175c1f6 (MD5) Previous issue date: 2008-02-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In the present work we analyze the dynamics of electrical cortical activity during the phenomenon of spreading depression (DA) and during the periods before and after this phenomenon. The characteristic of DA is reduced amplitude of spontaneous electrical activity that occurs in neural tissue after the application of stimulus that can be electrical, chemical, mechanical, luminous etc. In order to study properties of time series of electrical cortical activity recorded by ECoG (electrocortiogram) before,during and after DA, we apply Detrended Fluctuation Analysis(DFA). This method is designed to quantify long term correlations (memory) in temporal series such ECoG register. The method was successfully applied in studies of DNA sequences and non-stationary time series as heart rate variability, stride intervals, financial time series etc. The application of DFA results in scaling exponentα that quantifies correlation properties of nonlinear dynamical systems. This experiment indicates if temporal series posses long term correlations. In this work we calculate exponent α for different intervals: control (before the stimulus), after the stimulus, during the avalanche, during DA and after DA for two experimental groups of rats, nourished and malnourished. For both experimental groups the values of exponent α indicates persistent behavior for all intervals except during the avalanche in which correlations degrade. The presence of long term correlations in physiological time series observed in healthy organisms represents complexity that guaranties the organism’s adaptability to stress and disease. The absence of correlations during the avalanche indicates the loss of this complexity. Non-parametric Wilcoxon test was used to compare mean values of exponents α for all intervals of analyzed time series. In cases of nourished rats, the mean values ofα are significantly different for control, stimulus, avalanche, DA and after-DA intervals. Wilcoxon test was also used to compare mean values of α for corresponding intervals for the two experimental groups. The result is significant difference in mean values of α for control, stimulus avalanche, DA and after DA intervals between two experimental groups. The hypothesis that α =0.5 for avalanche intervals was not rejected by test, confirming the loss of correlations in this phase. Comparison of mean values of α for different intervals (control, stimulus, DA and after DA) with avalanche using the Wilcoxon test results in significant difference between two groups. / O presente estudo se propõe a analisar a dinâmica da atividade elétrica cortical durante o fenômeno da depressão alastrante (DA) e nos períodos que antecede e sucede o fenomêno. A DA é caracterizada pela redução da amplitude da atividade elétrica espontânea que ocorre no tecido neural, após a aplicação de um estímulo de natureza elétrica, química, mecânica, luminosa e outros. Visando estudar o comportamento da série temporal da atividade elétrica cortical, registrada no ECoG (eletrocorticograma), durante a DA e nos períodos que precede e sucede o fenômeno, foi aplicado o método do DFA (Detrended Fluctuation Analysis). Este método permite quantificar a existência de correlação de longo alcance (memória) numa série temporal, como é o caso do registro do ECoG. Anteriormente, o método foi aplicado em seqüências de DNA e no estudo de séries temporais não estacionárias, tais como, dinâmica da variabilidade cardíaca, flutuações de eletroencefalograma de humanos, intervalos entre passos sucessivos de humanos, séries econômicas e outros. A aplicação do DFA numa série temporal permite a determinação de um expoente de escalonamento α, que pode contribuir para a compreensão das propriedades dos sistemas dinâmicos não lineares. Este expoenteα revela se a série temporal apresenta correlação de longo alcance ou não. Neste trabalho os expoentes α foram calculados nas fases de controle, estímulo, avalanche, DA e após a DA para o ECoG, em dois grupos experimentais, ratos nutridos e ratos desnutridos. Em ambos os grupos experimentais, os valores obtidos para o expoente de escalonamento α denotam que a série temporal do ECoG apresenta correlação persistente (comportamento da série no presente se mantém no futuro) em todas as fases do processo com exceção da avalanche, período no qual ocorre perda de correlação. A presença de correlação de longo alcance numa série temporal biológica é uma resposta sempre observada em organismos saudáveis cuja complexidade do sinal registrado garante a adaptabilidade do organismo a situações de estresse e/ou distúrbios. Enquanto a ausência de correlação, observada na avalanche, indica a perda de propriedades fractais nos sistemas fisiológicos. O uso do método não-paramétrico de Wilcoxon, para comparar os valores médios dos expoentes α obtidos para o grupo de animais nutrido, durante as fases de controle, estimulação, DA, após DA, revelou que essas diferentes fasesdiferem significativamente. Os valores médios dos expoentes α obtidos para o grupo de animais desnutrido, durante as fases de controle, estimulação, DA, após DA, também não foram significativamente diferentes, quando comparados pelo método de Wilcoxon. Na comparação dos valores médios de α nas fases de controle, estimulação, DA, após DA entre os dois grupos de animais (nutrido e desnutrido) o teste de Wilcoxon revelou que as médias dos expoentes α em cada fase para os animais nutridos diferem significativamente daquelas obtidas para os animais desnutridos. Na avalanche a hipótese de que o expoente α é igual a 0,5, não foi rejeitada pelo teste de Wilcoxon, ou seja, o teste confirmou a perda de correlação nessa fase. Na comparação entre as médias dos expoentes α nos diferentes intervalos (controle, estimulação, DA, após DA) com o valor do expoente α na avalanche, o teste de Wilcoxon acusou diferença significativa tanto no grupo dos nutridos como no grupo dos desnutridos.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-21T12:02:13Z No. of bitstreams: 2 Dissertação - Renata da Costa Barbosa - 2014.pdf: 2041466 bytes, checksum: b05943f80e79bca8b9f3398d1706e187 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-21T12:10:02Z (GMT) No. of bitstreams: 2 Dissertação - Renata da Costa Barbosa - 2014.pdf: 2041466 bytes, checksum: b05943f80e79bca8b9f3398d1706e187 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-10-21T12:10:02Z (GMT). No. of bitstreams: 2 Dissertação - Renata da Costa Barbosa - 2014.pdf: 2041466 bytes, checksum: b05943f80e79bca8b9f3398d1706e187 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-11-28 / Understanding how the postural control system is impaired with aging can help identify elderly at risk of falling. In order to study the postural control, center of pressure (CP) behavior can be analyzed. Classical descriptors are commonly used for the CP analysis, however, modern descriptors have been developed aiming to provide more information about the underlying processes involved in the postural control. Aims: Analyze and compare classical and modern descriptors used to analyze the postural control in elderly subjects in quiet standing posture, using data acquired from a force platform in horizontal and inclined surfaces. Methods: The study sample consisted of 17 elderly subjects who remained on a force platform in the upright posture for 70 seconds. The data acquisition was performed with the platform on a horizontal surface and again on a surface inclined at 14 degrees with dorsiflexion and later with plantar flexion of the ankle. For each slope, the procedure was repeated three times with eyes open (EO ) and three times with eyes closed (EC). The initial 10 seconds were discarded and then, CP times series were analyzed in the anteroposterior (AP) and mediolateral (ML) directions. The classical descriptors used were in the time in the frequency domain and the modern descriptors were: Detrended Fluctuations Analysis (DFA), Stabilogram Diffusion Analysis (SDA) and the Sway Density Curve (SDC). Results: In the classical analysis, the results showed significant differences in all comparisons made and, in the modern analysis, the variables provided by the SDA and SDC also showed significant differences between comparisons, however, the DFA did not provide any difference between the conditions. Conclusion: Results provided by the classical variables and by the SDA and the SDC suggest a lower stability of elderly subjects in the inclined surface with dorsiflexion followed by plantar flexion and the eyes closed condition. More studies with the modern descriptors are necessary to better understand their results. / Entender como o sistema de controle postural é comprometido com o processo de envelhecimento pode contribuir na identificação de idosos com risco de quedas. Para estudar o controle postural pode-se analisar o comportamento do Centro de Pressão (CP). Descritores clássicos comumente são utilizados para a análise do CP, no entanto, descritores modernos têm sido desenvolvidos, com o intuito de fornecer mais informações sobre os processos subjacentes ao controle postural. Objetivos: Analisar e comparar descritores clássicos e modernos para análise do controle postural em sujeitos idosos na postura ereta quieta, utilizando dados adquiridos na plataforma de força no plano e com inclinação. Métodos: A amostra do estudo foi composta por 17 indivíduos idosos que permaneceram sobre uma plataforma de força na postura ortostática por 70 segundos. A aquisição dos dados foi realizada com uma plataforma sobre uma superfície horizontal e depois sobre uma superfície inclinada a 14 graus nas posições de flexão dorsal e flexão plantar do tornozelo. Para cada inclinação, o procedimento foi repetido três vezes com os olhos abertos (OA) e três vezes com os olhos fechados (OF). Depois de descartados os 10 s iniciais, foram analisadas as séries temporais do CP na direção anteroposterior (AP) e mediolateral (ML). Foram utilizados alguns descritores clássicos no domínio do tempo e da frequência e os descritores modernos: Detrended Fluctuation Analysis (DFA), Stabilogram Diffusion Analysis (SDA) e pela Sway Density Curve (SDC). Resultados: Na análise clássica os resultados indicaram diferenças significativas em todas as comparações realizadas, na análise moderna, as variáveis fornecidas pela SDA e pela SDC também apresentaram diferenças significativas entre as comparações, porém, a DFA não conseguiu apontar tais diferenças. Conclusão: Os resultados fornecidos pelas variáveis clássicas, SDA e a SDC sugerem uma menor estabilidade de sujeitos idosos na superfície inclinada com flexão dorsal seguida da flexão plantar e na condição de olho fechado. Ainda são necessários a realização de mais estudos utilizando tais descritores para uma melhor compreensão de seus resultados.