Avaliação do ritmo cardíaco em eletrocardiogramas de curta duração utilizando análise dos intervalos RR e aprendizado supervisionado

• Ano de defesa: 2019
• Autor(a) principal: Limeira, Gabriel de Azevedo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia Biomédica; UFRJ
• Palavras-chave em Português: Ritmo cardíaco; Fibrilação atrial; Classificação supervisionada; CNPQ::ENGENHARIAS
• Link de acesso: http://hdl.handle.net/11422/13234

Atrial fibrillation is a condition that often does not show itself through symptoms and is strongly related to infarction and sudden cardiac death. This work aims at developing an algorithm that differentiates atrial fibrillation rhythm from noise, normal and other rhythms in single short ECG leads collected by a mobile device. A total of 36 features were collected mostly from the sequence of beat-to-beat intervals. Neighborhood component analysis (NCA) feature selection technique was applied, and several supervised learning algorithms were compared and optimized using cross validation approach. Performances were compared with an index F1 that considers both sensitivity and specificity. NCA allowed selecting 11 features. The classifier based on support vector machines gave the best overall result (F1 = 72,9%), were the best performance occurred for the atrial fibrillation class (F1 = 82,5)