%0 Conference Proceedings
%4 sid.inpe.br/mtc-m18@80/2010/07.06.14.59
%2 sid.inpe.br/mtc-m18@80/2010/07.06.14.59.21
%T Uso de Mapas Auto-Organizáveis na Classificação de Quantificadores de Mapa de Recorrência de Batimentos Cardíacos
%D 2008
%A Liu, Y. L.,
%A Macau, E. E. N.,
%A Barroso, J. J.,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%E Vijaykumar, Nandamudi Lankalapalli,
%E Luz, Eduardo Fávero Pacheco da,
%E Furtado, Helaine Cristina Morais,
%E Yanasse, Horacio Hideki,
%E Domingues, Margarete Oliveira,
%E Rocha, Renata Sampaio da,
%E Follmann, Rosângela,
%E Cintra, Rosângela Saher Correa,
%E Veronese, Thalita Biazzuz,
%B Workshop dos Cursos de Computação Aplicada do INPE, 8 (WORCAP).
%C São José dos Campos
%8 15 e 16 out. 2008
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%X Cardiovascular diseases are the major cause of death in our country. Currently, a main ally to the realization of the pathophysiology studies of biological systems in the field of heart diseases is the technique known as the heart rate variability (HRV). However, the HRV has a complex behavior, making it difficult to identify patterns of specific diseases. In this work, we use the self-organized maps neural networks for the recurrence quantification analysis (RQA) on data from HRV, having as the main objective is to determine which of two sets of diagnoses (based on traditional or RQA) contains more information to distinguish between groups of patients.
%9 Modelagem Computacional
%@language pt
%3 Liu.pdf