Análise não-linear dos diferentes ritmos cerebrais nos registros do EEG em humanos com epilepsia e no ECoG de ratos em status epilepticus

• Ano de defesa: 2010
• Autor(a) principal: MORAES, Renato Barros
• Orientador(a): NOGUEIRA, Romildo de Albuquerque
• Banca de defesa: VARANDA, Wamberto Antônio, STOSIC, Tatijana
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal Rural de Pernambuco
• Programa de Pós-Graduação: Programa de Pós-Graduação em Biociência Animal
• Departamento: Departamento de Morfologia e Fisiologia Animal
• País: Brasil
• Palavras-chave em Português: Índice de parabolicidade; Epilepsia; Ondas cerebrais
• Palavras-chave em Inglês: Brain rhythms; Parabolicity index
• Área do conhecimento CNPq: CIENCIAS AGRARIAS::MEDICINA VETERINARIA
• Link de acesso: http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4661

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.