Machine learning approaches for ambulatory electrocardiography signal processing
Dublin Core
Título
Machine learning approaches for ambulatory electrocardiography signal processing
Materia
Ciencias técnicas
Ciencias Sociales
Descripción
Finally, this research presents a software tool for the analysis of the QT interval in the AECG. The software was developed for cardiologists and specialists,and no programming skilss are needed. Since QT markers are related to risk stratification of suffering life-threatening arrhythmias and sudden cardiac death, this tool constitutes a useful input to QT analysis. In this context, it will be useful for supporting the research on ventricular repolarization analysis.
Autor
Suárez León, Alexander Alexeis
Vázquez Seisdedos, Carlos Román (Director)
Huffel, Sabine Van (Director)
Editor
Editorial Universitaria
Fecha
2018
Colaborador
Cuba, Ministerio de Educación Superior
Derechos
Relación
Formato
pdf Interactivo (2,45 Mb)
Idioma
Inglés
Tipo
Texto
Identificador
isbn:9789591643315
Cobertura
Santiago de Cuba
Colección
Citación
Suárez León, Alexander Alexeis, Vázquez Seisdedos, Carlos Román (Director), y Huffel, Sabine Van (Director), “Machine learning approaches for ambulatory electrocardiography signal processing,” Catálogo EDUNIV, consulta 19 de septiembre de 2024, http://repositorio.eduniv.cu/items/show/1497.