Orientador: Konradin Metze / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-20T00:01:11Z (GMT). No. of bitstreams: 1 Valladao_MariaLuizadeCastroRamos_D.pdf: 10270983 bytes, checksum: 4313e15091d949287ba12830e233437d (MD5) Previous issue date: 2012 / Resumo: O tumor venéreo transmissível canino (TVTC) é transmitido por meio da transplantação de células neoplásicas. O sulfato de vincristina é o agente quimioterápico mais utilizado no tratamento do TVTC. O objetivo deste estudo foi comparar as alterações morfológicas antes da quimioterapia e após uma semana da aplicação de uma injeção intravenosa de sulfato de vincristina. Neste estudo prospectivo, 100 massas tumorais do TVTC de ocorrência natural foram inclusos. Lâminas citológicas coradas com HE, cortes parafinados corados com tubulina e PCNA, cortes para microscopia eletrônica foram preparados pré e pós tratamento. Realizou-se a contagem da taxa de mitoses, da porcentagem de linfócitos e da marcação do PCNA. Também foi feita a análise de imagem computacional da textura nuclear e a análise sintática estrutural do tecido tumoral. A morfometria demonstrou que, após a terapia, houve a diminuição da área, do perímetro, do diâmetro, da densidade óptica integrada e da tonalidade dos agrupamentos nucleares; enquanto houve aumento do "goodness of fit" da dimensão fractal nuclear. Na análise sintática estrutural, observou-se o aumento da distância entre os núcleos. A contagem de mitoses e o índice PCNA não mostraram diferença entre a primeira e a segunda biópsia. Os agrupamentos citoplasmáticos grandes que foram corados com tubulina diminuíram de tamanho após o tratamento e essa alteração talvez se deva à ruptura induzida pela vincristina nos microtúbulos. Em resumo, a quimioterapia com vincristina promoveu profundas alterações no núcleo, no citoplasma e na reorganização estrutural sintática do tumor / Abstract: The canine transmissible venereal tumor (CTVT) is transmitted by transplantation of neoplastic cells. Vincristine sulfate is the most widely used chemotherapeutic agent for the treatment of CTVT. The aim of this study was to compare the tumor morphology before chemotherapy and one week after application of a single intravenous injection of vincristine sulphate. In this prospective study 100 animals with spontaneously occuring CTVT were included. HE-stained cytological imprints, paraffin sections stained by tubulin and PCNA and sections for electron microscopy were prepared from the tumor before and after treatment. Mitotic rate, percentage of lymphocytes and PCNA stained tumor cells were counted. Computerized image analysis of the nuclear texture and syntactic structure analysis were applied. After therapy nuclear morphometry showed decreased area, perimeter and diameter values, diminished integrated optical density and cluster shade of nuclear texture, as well as increased values of "goodness of fit" of the fractal dimension. The nuclei were more distant. Mitotic counting and PCNA index did not differ between the first and second biopsy. Cytoplasmic large tubulin clusters diminished their size during treatment. This finding may be due to the vincristine-induced rupture of microtubules. In summary, chemotherapy with vincristine provides profound changes in the nuclear, cytoplasmic and syntactic structural organization of the tumor / Doutorado / Biologia Estrutural, Celular, Molecular e do Desenvolvimento / Doutor em Fisiopatologia Medica

Medical images, like any other digital data, require compression in order to reduce disk space needed for storage and time needed for transmission. The lossless compression methods of still images can shorten the file only to a very limited degree. The application of fractal compression to medical images would allow obtaining much higher compression ratios. While the fractal magnification – an inseparable feature of the fractal compression – would be very useful in presenting the reconstructed image in a highly readable form. However, like all irreversible methods, the fractal compression is connected with the problem of information loss, which is especially troublesome in the medical imaging. A very time consuming encoding process, which can last even several hours, is another bothersome drawback of the fractal compression. Based on a survey of literature and own cogitations, the author attempts to provide an adapted to the needs of medical imaging solution that will overcome the unfavorable ailments of the fractal compression methods. The thesis does not provide only theoretical deliberations but also gives implementation of the proposed algorithm, which is used to test the suitability of the fractal compression to medical imaging. The results of the work are more than satisfying – the fidelity of the images compressed with the proposed fractal compression method meets the requirements imposed on the medical images and the fractal magnification outperforms other magnification techniques.

It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation. It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation.

[pt] Esta tese tem como objetivo propor uma reflexão teórica sobre o discurso biográfico e sua aplicação na própria produção de uma biografia. Para isso, está dividida em duas partes. Na primeira, são expostos os referenciais teóricos que tentam mapear a complexidade da construção deste tipo de discurso, levando em conta que estamos lidando com múltiplas e flexíveis redes de interpretação e discussão. Na segunda parte,elaboramos uma biografia em fractais de Adolpho Bloch, com o propósito de abordar as múltiplas e complexas identidades do biografado. Nossa proposta busca fugir da ineficácia de explicações unilaterais e totalizadoras e defende que as interpretações tenham como referência a complexidade. / [en] This dissertation intends to propose a theorical reflection about de discourse of biographies and its aplication by producting a biography. In order to do this, the dissertation has two parts. In the first one, we expose the theorical references that try to map the complexy of this kind of discourse construction, taking the account that we are working with multiplies and flexives nets of interpretation and discussion. In the second part, we ve made a fractal biography of Adolph Bloch, with the intention of study the multiples and complex identities of him. Our porpouse wants to escape from the ineficience of the total and unilateral explanations, and bases its references in the complexity of the interpretations.

The theoretical focus of this study is Meaning in Architecture. The study does not draw from history or popular culture for meaning, but will aim to transcend Post-modern concepts by focusing on the contemporary condition and archetypal forms as a result of history and popular culture. This is done through the use of fractal geometry and deals with the questions that arise regarding signification. Fractals can be considered a subdivision of the language of mathematics and will be utilised as mediation between the reality of our world and the generation of form for the purpose of design. This is explored in two analogies, Architecture and Mathematics; and Architecture and Language. Through this process, subjectivity relating to form is removed as the design was developed in conjunction with the area in which it manifests, through the transformation of quantifiable entities into form. The programme and the process have become a unified whole in that mathematical concepts were utilised to design a building to house people involved with mathematics. The aim of the design proposal is to contribute to the urban landscape of Tshwane by allowing access to facilities in which training in mathematics and computer science can be achieved and to allow individuals to come to a place of self-actualization. The design is defined as a snapshot of the contemporary condition and therefore makes use of passive and active technologies to create a habitable environment. It is imperative to realise that society is in a process of transition and this can be embraced by combining ‘green’ and ‘non-green’ design approaches, while working towards more energy-efficient design solutions. Copyright / Dissertation (MArch(Prof))--University of Pretoria, 2010. / Architecture / unrestricted

O desenvolvimento da tecnologia e a propagação de sistemas computacionais nos mais variados domínios do conhecimento têm contribuído para a geração e o armazenamento de uma quantidade constantemente crescente de dados, em uma velocidade maior da que somos capazes de processar. De um modo geral, a principal razão para o armazenamento dessa enorme quantidade de dados é a utilização deles em benefício da humanidade. Diversas áreas têm se dedicado à pesquisa e a proposta de métodos e processos para tratar esses dados. Um desses processos é a Descoberta de Conhecimento em Bases de Dados, a qual tem como objetivo extrair conhecimento a partir das informações contidas nesses dados. Para alcançar esse objetivo, usualmente são construídos modelos (hipóteses), os quais podem ser gerados com o apoio de diferentes áreas tal como a de Aprendizado de Máquina. A Seleção de Atributos desempenha uma tarefa essencial dentro desse processo, pois representa um problema de fundamental importância em aprendizado de máquina, sendo freqüentemente realizada como uma etapa de pré-processamento. Seu objetivo é selecionar os atributos mais importantes, pois atributos não relevantes e/ou redundantes podem reduzir a precisão e a compreensibilidade das hipóteses induzidas por algoritmos de aprendizado supervisionado. Vários algoritmos para a seleção de atributos relevantes têm sido propostosna literatura. Entretanto, trabalhos recentes têm mostrado que também deve-se levar em conta a redundância para selecionar os atributos importantes, pois os atributos redundantes também afetam a qualidade das hipóteses induzidas. Para selecionar alguns e descartar outros, é preciso determinar a importância dos atributos segundo algum critério. Entre os vários critérios de importância de atributos propostos, alguns estão baseados em medidas de distância, consistência ou informação, enquanto outros são fundamentados em medidas de dependência. Outra questão essencial são as avaliações experimentais, as quais representam um importante instrumento de estimativa de performance de algoritmos de seleção de atributos, visto que não existe análise matemática que permita predizer que algoritmo de seleção de atributos será melhor que outro. Essas comparações entre performance de algoritmos são geralmente realizadas por meio da análise do erro do modelo construído a partir dos subconjuntos de atributos selecionados por esses algoritmos. Contudo, somente a consideração desse parâmetro não é suficiente; outras questões devem ser consideradas, tal como a percentagem de redução da quantidade de atributos desses subconjuntos de atributos selecionados. Neste trabalho é proposto um algoritmo que separa as análises de relevância e de redundância de atributos e introduz a utilização da Dimensão Fractal para tratar atributos redundantes em aprendizado supervisionado. É também proposto um modelo de avaliação de performance de algoritmos de seleção de atributos baseado no erro da hipótese construída e na percentagem de redução da quantidade de atributos selecionados. Resultados experimentais utilizando vários conjuntos de dados e diversos algoritmos consolidados na literatura, que selecionam atributos importantes, mostram que nossa proposta é competitiva com esses algoritmos. Outra questão importante relacionada à extração de conhecimento a partir de bases de dados é o formato no qual os dados estão representados. Usualmente, é necessário que os exemplos estejam descritos no formato atributo-valor. Neste trabalho também propomos um metodologia para dar suporte, por meio de um processo semi-automático, à construção de conjuntos de dados nesse formato, originados de informações de pacientes contidas em laudos médicos que estão descritos em linguagem natural. Esse processo foi aplicado com sucesso a um caso real. / Progress in computer systems and devices applied to a different number of fields, have made it possible to collect and store an increasing amount of data. Moreover, this technological advance enables the storage of a huge amount of data which is difficult to process unless new approaches are used. The main reason to maintain all these data is to use it in a general way for the benefit of humanity. Many areas are engaged in the research and proposal of methods and processes to deal with this growing data. One such process is Knowledge Discovery from Databases, which aims at finding valuable and interesting knowledge which may be hidden inside the data. In order to extract knowledge from data, models (hypothesis) are usually developed supported by many fields such as Machine Learning. Feature Selection plays an important role in this process since it represents a central problem in machine learning and is frequently applied as a data pre-processing step. Its objective is to choose a subset from the original features that describes a data set, according to some importance criterion, by removing irrelevant and/or redundant features, as they may decrease data quality and reduce comprehensibility of hypotheses induced by supervised learning algorithms. Most of the state-of-art feature selection algorithms mainly focus on finding relevant features. However, it has been shown that relevance alone is not sufficient to select important features. Different approaches have been proposed to select features, among them the filter approach. The idea of this approach is to remove features before the model's induction takes place, based on general characteristics from the data set. For the purpose of selecting features and discarding others, it is necessary to measure the features' goodness, and many importance measures have been proposed. Some of them are based on distance measures, consistency of data and information content, while others are founded on dependence measures. As there is no mathematical analysis capable of predicting whether a feature selection algorithm will produce better feature subsets than others, it is important to empirically evaluate the performance of these algorithms. Comparisons among algorithms' performance is usually carried out through the model's error analysis. Nevertheless, this sole parameter is not complete enough, and other issues, such as percentage of the feature's subset reduction should also be taken into account. In this work we propose a filter that decouples features' relevance and redundancy analysis, and introduces the use of Fractal Dimension to deal with redundant features. We also propose a performance evaluation model based on the constructed hypothesis' error and the percentage of reduction obtained from the selected feature subset. Experimental results obtained using well known feature selection algorithms on several data sets show that our proposal is competitive with them. Another important issue related to knowledge extraction from data is the format the data is represented. Usually, it is necessary to describe examples in the so-called attribute-value format. This work also proposes a methodology to support, through a semi-automatic process, the construction of a database in the attribute-value format from patient information contained in medical findings which are described in natural language. This process was successfully applied to a real case.

Submitted by Adriana Martinez (amartinez@unoeste.br) on 2018-05-10T22:40:35Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Isabella Saneshigue.pdf: 688383 bytes, checksum: dcc746f15532dd8ee811a23185cc4201 (MD5) / Made available in DSpace on 2018-05-10T22:40:35Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Isabella Saneshigue.pdf: 688383 bytes, checksum: dcc746f15532dd8ee811a23185cc4201 (MD5) Previous issue date: 2017-11-24 / Accidents caused by venom of Crotalus snakes, popularly known in Brazil as rattlesnake, cause the highest number of deaths in humans and animals, mainly due to the great neurotoxic, myotoxic, coagulant, nephrotoxic and hepatotoxic potential of their venom. The present study had the objective of analyzing by histology and fractal dimension liver samples of Wistar rats experimentally poisoned with venom of the snake Crotalus durissus terrificus. The hypothesis is that the venom of Crotalus durissus terrificus is capable of inducing hepatic damage at the dose recommended in this study, that its alterations can be quantified by the fractal dimension and that the antiofidic serum be able to minimize the hepatic lesions induced by the venom. Ninety rats were divided into different groups and treated with: control group (GC, n = 30) 0.9% sodium chloride solution; venom group (GV, n = 30) crotalic venom; (GVS, n = 30) Crotalic venom and antiofidic serum 6 hours after the application of the venom. Liver samples were collected at 2h (M1), 8h (M2) and 24h (M3) after venom administration and submitted to histological analysis and fractal dimension (DF) using the ImageJ® software and box-counting method. Procedures for collecting, processing and analyzing samples were standardized. No significant lesions were observed in GC and GV. Necrosis, cytoplasmic and nuclear vacuolization and absence of inflammatory infiltrate were observed in M2 and M3, whereas in GVS, mononuclear inflammatory infiltrate was evident at all times, in addition to the lesions found in GV. The lesions of necrosis, cytoplasmic and nuclear vacuolization, considered of greater severity were visualized in M3 in both GV and GVS. There was an increase in DF for the same changes in GV and GVS over time, but with no difference between them, but with a significant difference compared to CG. The lesions evidenced in the liver were not minimized by the application of the antiofidic serum. This study agrees with other authors about the hepatotoxicity of crotalic venom in relation to histological findings and the results indicate an increase in FD for the findings of vacuolization and necrosis, proving to be an efficient method for the quantitative evaluation of morphological changes induced by venom without observer interference. In addition, non-protection of the liver by antiofidic serum was evident. It is concluded that Crotalus durissus terrificus venom has hepatotoxic effects; FD is effective in the quantitative morphological evaluation of the liver for vacuolization and necrosis, and antiofidic serum did not protect the liver from venom-induced lesions. / Os acidentes causados pelo veneno das serpentes do gênero Crotalus, conhecidas popularmente no Brasil como cascavel, causam o maior índice de óbitos em seres humanos e animais, principalmente pelo grande potencial neurotóxico, miotóxico, coagulante, nefrotóxico e hepatotóxico do seu veneno. O presente estudo teve por objetivo analisar pela a dimensão fractal e achados histológicos em amostras de fígado de ratos Wistar intoxicados experimentalmente com veneno da serpente Crotalus durissus terrificus. A hipótese é que o veneno da Crotalus durissus terrificus seja capaz de induzir lesão hepática na dose preconizada neste estudo, que suas alterações possam ser quantificadas pela dimensão fractal e que o soro antiofídico seja capaz de minimizar as lesões hepáticas induzidas pelo veneno. Noventa ratos, foram distribuídos em diferentes grupos e tratados com: grupo controle (GC, n=30) solução de cloreto de sódio 0,9%; grupo veneno (GV, n=30) veneno crotálico; grupo veneno/soro antiofídico (GVS, n=30) veneno crotálico e soro antiofídico 6 horas após aplicação do veneno. As amostras do fígado foram realizadas nos momentos 2h (M1), 8h (M2) e 24h (M3) após administração do veneno e submetidas a análise histológica e dimensão fractal (DF) utilizou o software ImageJ® e método de box-counting. Os procedimentos para coletar, processar e analisar as amostras foram padronizados. Não foram observadas lesões significativas no GC e no GV foram evidenciadas necrose, vacuolização citoplasmática e nuclear e ausência de infiltrado inflamatório nos M2 e M3, enquanto que no GVS evidenciou-se infiltrado inflamatório mononuclear em todos os momentos, além das lesões constatadas no GV. As lesões de necrose, vacuolização citoplasmática e nuclear, consideradas de maior severidade foram visualizadas no M3 tanto no GV quanto no GVS. Observou-se uma elevação da DF para as mesmas alterações nos GV e GVS ao longo do tempo, porém sem diferença entre eles, mas com diferença significativa comparado ao GC. As lesões evidenciadas em fígado não foram minimizadas pela aplicação do soro antiofídico. Este estudo concorda com outros autores quanto a hepatotoxicidade do veneno crotálico frente aos achados histológicos e os resultados apontam aumento da DF para os achados de vacuolização e necrose, mostrando ser um método eficaz na avaliação quantitativa de alterações morfológicas induzidas pelo veneno, sem a interferência do observador. Além disso, a não proteção do fígado pelo soro antiofídico foi evidente. Conclui-se que o veneno de Crotalus durissus terrificus apresenta efeitos hepatotóxicos; a DF é eficaz na avaliação morfológica quantitativa do fígado para vacuolização e necrose, e o soro antiofídico não protegeu o fígado de lesões induzidas pelo veneno.

Les structures sandwichs à cœur cellules nid d'abeilles sont largement employées dans l'industrie aéronautique, notamment pour leur grande rigidité de flexion à faible masse. Cette dernière caractéristique résulte également en une diminution de la transmission et une augmentation du rayonnement acoustique de ces structures. On cherche à modéliser une nouvelle stratégie passive de réduction des vibrations mécaniques et du rayonnement, se basant sur le principe de localisation des vibrations du matériau. L'idée fondatrice est l'insertion au sein du matériau d'hétérogénéités créant des obstacles pour la propagation des ondes mécaniques et donc des phénomènes de réflexion, transmission, diffusion voire dissipation de l'énergie mécanique vibratoire. Cela résultera en une localisation de certains modes structuraux, et modifiera le rayonnement acoustique associé à ces modes. Les travaux de thèse porte sur la modélisation, la simulation numérique et la validation expérimentale de structures sandwichs présentant des sur-charges localisées suivant un schéma fractal. / Sandwich structures with honeycomb core cells have been widely used within the aerospace industry, because of their high static bending stiffness at a lesser mass cost. The weight reduction is unfortunately responsible for a decrease in the acoustic transmission and a rise of the radiation of such structures. Therefore, a new passive strategy of structural vibration and acoustic radiation reduction is investigated. The founding idea is to used the geometrical network of the honeycomb core by filling some cells with small heterogeneities, in order to create obstacles for the structural waves, creating wave reflection, transmission or even dissipation of the mechanical energy. That results in localization of some modes, which is modifying their acoustic radiation. This study implies modeling, numerical simulation and experimental validation of sandwich structures locally overloaded by fractal distributions.

No description available.

Physiological signals, electroencephalogram (EEG) and heart rate variability (HRV), are generated by complex self-regulating systems. These signals are extremely inhomogeneous and nonstationary, and fluctuate in an irregular and highly complex manner. These fluctuations are due to underlying dynamics of the system. The synchronous neural activity measured as scalp EEG indicates underlying neural dynamics of the brain. Hence, quantitative EEG analysis has become a very useful tool in interpreting results from physiological experiments. The analysis of HRV provides valuable information to assess the autonomous nervous system (ANS). The HRV can be significantly affected by physiological state changes and many disease states. Hence, HRV analysis is becoming a major experimental and diagnostic tool. In this thesis, we focus on the study of EEG and HRV time series using tools from nonlinear time series analysis with special emphasis on its implications in detecting physiological state changes such as, in diseases like epileptic seizure and schizophrenia, and in altered states of consciousness as in sleep and meditation. The proposed nonlinear techniques are used in discriminating different physiological states from control states. Artifact processing of EEG signal Interferences (artifacts) from various sources unavoidably contaminate EEG recordings. In quantitative analysis, results can differ significantly by these artifacts, which may lead to wrong interpretation of the results. In this part of the thesis, we have devised methods to minimize ocular and muscle artifacts in EEG. The artifact correction methods are based on blind source separation (BSS) techniques such as singular value decomposition (SVD), algorithm for multiple signal extraction (AMUSE), canonical correlation analysis (CCA), information maximization (INFOMAX) independent component analysis (ICA) and joint approximate diagonalization of eigen-matrices (JADE) ICA. We have proposed a method to simulate clean and artifact corrupted EEG data based on the BSS methods. In order to enhance the performance of BSS methods, a technique called wavelet-filtered component inclusion method has been introduced. In addition, second-order statistics (SOS) and higher-order statistics (HOS) based BSS methods have been studied considering less number of EEG channels; and performance comparison of these methods has also been made. We have also addressed the problem of simultaneous correction of ocular and muscle artifacts in EEG recordings using the BSS methods. Irrespective of the BSS methods, the component elimination method has introduced high spectral error in all the bands after reconstruction of clean EEG. However, the wavelet filtered component inclusion method has retained almost all spectral powers of EEG channels in theta, alpha, and beta bands after ocular artifact minimization. When the number of EEG channels is very less, the enhanced CCA (SOS BSS) has given superior artifact minimization results than HOS BSS methods, especially in delta band. The component elimination method is used in muscle artifact minimization, and hence the SVD method cannot be used for this purpose since it leads to large spectral distortion of reconstructed EEG. The AMUSE and CCA methods have given comparable performance in muscle artifact minimization. In addition, the JADE method has introduced less mean spectral error compared to other methods. The CCA method has shown superior performance in simultaneous minimization of ocular and muscle artifacts, and AMUSE and JADE methods have given comparable results. Furthermore, the less computation time of wavelet enhanced SOS BSS methods make them very useful in real clinical environments. Fractal characterization of time series In biomedical signal analysis, fractal dimension (FD) is used as a quantitative measure to estimate complexity of physiological signals. Such analysis helps to study physiological processes of underlying systems. The FD can also be used to study dynamics of transitions between different states of systems like brain and ANS, in various physiological and pathological states. In this part, we have proposed a method to estimate FD of time series, called multiresolution box-counting (MRBC) method. A modification of this method resulted in multiresolution length (MRL) method. The estimation performance of the proposed methods is compared with that of Katz, Sevcik, and Higuchi methods, by simulating mathematically defined fractal signals, and also the computation time is compared between the methods. The MRBC and MRL methods have given comparable performance to that of Higuchi method, in estimating FD of waveforms, with the advantage of less computational time. In addition, various properties of the FD are studied and discussed in connection with classical signal processing concepts such as amplitude, frequency, sampling frequency, effect of noise, band width, correlation, etc. The FD value of signals has increased with number of harmonics, noise variance, band-width, and mid-band frequency, and decreased with degree of correlation in AR signal. An analogy between Katz FD and smoothed Teager energy operator has also been made. Application of fractal analysis to EEG and HRV time series The fluctuation of EEG potentials normally depends upon degree of alertness, and varies in amplitude and frequency. Hence, the EEG is an important clinical tool for studying sleep and sleep related disorders, epileptic seizures, schizophrenia, and meditation. In this part of the thesis, we have used FD which gives signal complexity, and detrended fluctuation analysis (DFA) which gives multiscale exponent of time series to quantify EEG. We have extended the concept of FD to multiscale FD to compute complexity of time series at multiple scales. The main applications of the proposed method are epileptic seizure detection, sleep stage detection, schizophrenia EEG analysis, and analysis of heart rate variability during meditation. For seizure detection, we have used intracranial EEG recordings with seizure-free and seizure intervals. In sleep EEG analysis, whole-night sleep EEG is used and results are compared with the manually scored hypnogram. The schizophrenia symptom is further categorized into positive and negative symptoms and complexity is estimated using FD and DFA. We have also analyzed HRV data of Chi and Kundalini meditation using FD and DFA techniques. In all the applications considered, we have tested for statistical significance of the computed parameters, between the case of interest and corresponding control cases, to discriminate between the physiological states. The ocular artifact has reduced FD while muscle artifact increased FD of EEG. The FD of seizure EEG has shown high value compared to that of seizure-free EEG. In addition, the seizure-free EEG has more DFA exponent-1 than seizure EEG. The value of FD of EEG is decreased with deepening of sleep, wake state having high FD value. The FD of REM state sleep EEG showed value between that of wake and state-1. The DFA exponent-1 has increased with deepening of sleep state, having small value for wake state. The REM state has given exponent-1 value between wake and state-1. The schizophrenia subjects have shown lower FD value than healthy controls in all the EEG channels except the bilateral temporal and occipital regions. The positive symptom sub-group has shown comparatively high FD values than healthy controls as well as overall schizophrenia sample in the bilateral tempero-parietal-occipital region. In addition, the positive symptom sub-group has shown significantly higher regional FD values than negative symptom sub-group especially in right temporal region. The overall schizophrenia samples as well as the positive and negative subgroup have shown least FD values in the bilateral frontal region. The values of DFA exponent-2 have shown significant high value in schizophrenia samples. In addition, the schizophrenia group has shown less DFA exponent-1 in bilateral temporal region than healthy control. The FD, multiscale FD, DFA exponents have shown significant performance in discriminating different physiological states from control states. The FD value of HRV time series during meditation is less compared to pre-meditation state in both Chi and Kundalini meditation. Irrespective of the type of meditation, meditation state has shown significantly high DFA exponent-1 than pre-meditation state, and significantly high DFA exponent-2 in pre-meditation state compared to meditation state. Functional connectivity analysis of brain during meditation In functionally related regions of the brain, even in those regions separated by substantial distances, the EEG fluctuations are synchronous, which is termed as functional connectivity. In this part, a novel application of functional connectivity analysis of brain using graph theoretic approach has been made on the EEG recorded from meditation practitioners. We have used 16 channel EEG data from subjects while performing Raja Yoga meditation. The pre-meditation condition is used as control state, against which meditation state is compared. For finding connectivity between EEG of various channels, we have computed pair-wise linear correlation and mutual information between the EEG channels, to form a connection matrix of size 16x16. Then, various graph parameters, such as average connection density, degree of nodes, characteristic path length, and cluster index, are computed from the connection matrix. The computed parameters are projected on to the scalp to get topographic head maps that give spatial variation of the parameter, and results are compared between meditation and pre-meditation states. The meditation state has shown low average connection density, less characteristic path length, and high average degree in fronto-central and central regions. Furthermore, high cluster index is shown in frontal and central regions than pre-meditation state. The parameters such as complexity, characteristic path length and average connection density are used as features in quadratic discriminant classifier to classify meditation and pre-meditation state, and have given good accuracy performance. Connectivity analysis using mutual information has given high average connection density in meditation state in theta, alpha and beta bands compared to pre-meditation state. The characteristic path length is high in delta, alpha and beta bands in meditation state. In addition, the meditation state has shown high degree and cluster index in theta and beta bands compared to pre-meditation state. Nonlinear dynamical characterization of HRV during meditation The cardiovascular system is influenced by internal dynamics as well as from various external factors, which makes the system more dynamic and nonlinear. In this part of the thesis, a novel application of using HRV data for studying Chi and Kundalini meditation has been made. The HRV time series are embedded into higher dimensional phase-space using Takens’ embedding theorem to reconstruct the attractor. After estimating the minimum embedding dimension to unfold the attractor dynamics, the complexity of the attractor is computed using correlation dimension, Lyapunov exponent, and nonlinearity scores. In all the analyses, the pre-meditation state is used as control state against which meditation state is compared. The statistical significance of the parameters estimated is tested to discriminate meditation state from control state. The HRV time series of both pre-meditation and meditation have shown similar minimum embedding dimensions in both Chi and Kundalini meditation. Irrespective of the type of meditation, the meditation state has shown high correlation dimension, largest Lyapunov exponent, and low nonlinearity score compared to pre-meditation state. Recurrent quantification analysis of HRV during meditation In this part, a novel application of recurrent quantification analysis (RQA) to HRV during meditation is studied. Here, the time series is embedded into a higher dimensional phase-space and Euclidean distance between the embedded vectors is calculated to form a distance matrix. The matrix is converted into binary matrix by applying a suitable threshold, and plotted as image to get recurrence plot. Various parameters are extracted from the recurrence plot such as percent recurrence rate, diagonal parameters (determinism, divergence, entropy, ratio), and vertical or horizontal parameters (laminarity, trapping time, maximal vertical line length). The procedure is applied to HRV data during meditation and pre-meditation (control) to discriminate between the states. The HRV of meditation state has shown more diagonal line structure whereas more black patches are observed in pre-meditation state. In addition, at low embedding dimensions, the meditation state has shown low recurrence rate, high determinism, low divergence, low entropy, high ratio, high laminarity, high trapping time, and less maximal vertical line length compared to pre-meditation state. These RQA parameters have shown superior performance in discriminating meditation state from control state.