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Submitted by (lucia.rodrigues@ufrpe.br) on 2016-06-09T14:34:56Z No. of bitstreams: 1 Renato Barros Moraes.pdf: 1461731 bytes, checksum: 72fe61b249cbff251455227ca064db5a (MD5) / Made available in DSpace on 2016-06-09T14:34:56Z (GMT). No. of bitstreams: 1 Renato Barros Moraes.pdf: 1461731 bytes, checksum: 72fe61b249cbff251455227ca064db5a (MD5) Previous issue date: 2010-02-09 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Over the last 25 years, major advances have occurred in the techniques of nonlinear analysis applied to time series. These techniques have helped us to understand how dynamic systems behave over time. The brain is considered the most complex dynamic system known for man, and as such, it presents great challenges to the understanding of their processes, both physiological and pathological. In this work, we try to better understand epilepsy, a brain disease that affects millions of individuals around the world. The records of electroencephalogram (EEG) and electrocorticogram (ECoG) are widely used in the clinic for diagnosis and monitoring of epilepsy, but the information contained in these records are underutilized, since they are generally analyzed by the clinical eye. It is known that is contained in the EEG and ECoG, some specific frequencies such as alpha (α), beta (β), theta (θ), delta (δ) and gamma (γ) and they have interesting properties for the diagnosis of some brain pathologies. Through the DFA (Detrended fluctuation Analysis) technique used to verify long-range correlation in time series, and a derivation of this, the Parabolicity index (b), we observed some differences in EEG and ECoG signals, to normal and epileptic conditions between different brain rhythms, both in an animal model and in human records. / Nos últimos 25 anos, grandes avanços têm ocorrido nas técnicas de análise não-linear aplicadas a séries temporais. Essas técnicas têm nos ajudado a entender como sistemas dinâmicos se comportam com o passar do tempo. O cérebro é considerado o sistema dinâmico mais complexo conhecido pelo homem, e como tal apresenta grandes desafios para a compreensão de seus processos, tanto fisiológicos quanto patológicos. Nesse trabalho, tentamos compreender melhor a epilepsia, uma patologia cerebral que afeta milhões de indivíduos em todo o mundo. Os registros de eletroencefalograma (EEG) e eletrocorticograma (ECoG) são bastante utilizados na clínica para o diagnóstico e acompanhamento da epilepsia, porém as informações contidas nestes registros são subutilizadas, uma vez que são analisadas geralmente pelo olho clínico. Sabe-se que estão contidas no EEG e ECoG, algumas freqüências específicas tais como alfa(α), beta(β), teta(θ), delta(δ) e gama(γ), e que elas possuem propriedades interessantes para diagnóstico de algumas patologias cerebrais. Através da DFA (Análise de Flutuação sem Tendência), técnica usada para verificar correlação de longo alcance em séries temporais, e de uma derivação dessa, o Índice de parabolicidade (b), conseguimos verificar algumas diferenças nos sinais de ECoG e EEG, para uma condição normal e epiléptico, entre as diferentes ondas cerebrais, tanto num modelo animal quanto em registros de humanos.

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. These signals also have transient structures related to spiking or sudden onset of a stimulus, which have a duration not exceeding tens of milliseconds. Further, brain signals are highly non-stationary because both behavioral state and external stimuli can change over a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal. In Chapter 2, we describe a multi-scale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both sharp stimulus-onset transient and sustained gamma rhythm in local field potential recorded from the primary visual cortex. Gamma rhythm (30 to 80 Hz), often associated with high-level cortical functions, has been proposed to provide a temporal reference frame (“clock”) for spiking activity, for which it should have least center frequency variation and consistent phase for extended durations. However, recent studies have proposed that gamma occurs in short bursts and it cannot act as a reference. In Chapter 3, we propose another gamma duration estimator based on matching pursuit (MP) algorithm, which is tested with synthetic brain signals and found to be estimating the gamma duration efficiently. Applying this algorithm to real data from awake monkeys, we show that the median gamma duration is more than 330 ms, which could be long enough to support some cortical computations.