Classificação de sinais EGG combinando Análise em Componentes Independentes, Redes Neurais e Modelo Oculto de Markov

• Ano de defesa: 2015
• Autor(a) principal: Santos, Hallan Cosmo dos
• Orientador(a): Montesco, Carlos Alberto Estombelo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Sergipe
• Programa de Pós-Graduação: Pós-Graduação em Ciência da Computação
• País: BR
• Palavras-chave em Português: Análise de componentes independentes; FastIca; Métodos tensoriais; Redes neurais artificiais; Eletrogastrografia; Modelo Oculto de Markov
• Palavras-chave em Inglês: Independent component analysis; FastIca; Tensorial methods; Artificial neural networks; Electrogastrogram; Hidden Markov Model
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://ri.ufs.br/handle/riufs/3348

Identify some digestive features in people through Electrogastrogram (EGG) is important because this is a cheap, non-invasive and less bother way than traditional endoscopy procedure. This work evaluates the learning behavior of Artificial Neural Networks (ANN) and Hidden Markov Model (HMM) on components extracted by Independent Component Analysis (ICA) algorithms. In this research, an experiment was made with statistical analysis that shows the relationship between neutral, negative or positive images and digestive reactions. Training some classifiers with an EGG signal database, where the emotional states of individuals are known during processing, would it be possible to carry out the other way? Meaning, just from the EGG signal, estimate the emotional state of individuals. The initial challenge is to treat the EGG signal, which is mixed with the signals from other organs such as heart and lung. For this, the FastICA and Tensorial Methods algorithms were used, in order to produce a set of independent components, where one can identify the stomach component. Then, the EGG signal classification is performed through ANN and HMM models. The results have shown that extracting only the stomach signal component before the experiment can reduce the learning error rate in classifiers.