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Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation

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Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation

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dc.contributor.author	Caravaca Moreno, Juan
dc.contributor.author	Soria Olivas, Emilio
dc.contributor.author	Bataller Mompean, Manuel
dc.contributor.author	Serrano López, Antonio José
dc.contributor.author	Such Miquel, Luis
dc.contributor.author	Vila Francés, Joan
dc.contributor.author	Guerrero Martínez, Juan Francisco
dc.date.accessioned	2015-03-02T12:09:20Z
dc.date.available	2015-03-02T12:09:20Z
dc.date.issued	2014
dc.identifier.citation	Caravaca Moreno, Juan Soria Olivas, Emilio Bataller Mompeán, Manuel Serrano López, Antonio J. Such Miquel, Luis Vila Francés, Joan Guerrero Martínez, Juan Francisco 2014 Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation Computers in Biology and Medicine 45 1 1 7
dc.identifier.uri	http://hdl.handle.net/10550/42558
dc.description.abstract	This work presents the application of machine learning techniques to analyze the influence of physical exercise in the heart's physiological properties, during ventricular fibrillation. With that purpose, different kinds of classifiers (linear and neural models) were used to classify between trained and sedentary rabbit hearts. These classifiers were used to perform knowledge extraction through a wrapper feature selection algorithm. The obtained results showed the higher performance of the neural models compared to the linear classifier (higher performance measures and higher dimensionality reduction). The most relevant features to describe the benefits of physical exercise are those related to myocardial heterogeneity, mean activation rate and activation complexity.
dc.language.iso	eng
dc.relation.ispartof	Computers in Biology and Medicine, 2014, vol. 45, num. 1, p. 1-7
dc.subject	Cor Malalties
dc.subject	Enginyeria biomèdica
dc.title	Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation
dc.type	journal article	es_ES
dc.date.updated	2015-03-02T12:09:20Z
dc.identifier.doi	10.1016/j.compbiomed.2013.11.008
dc.identifier.idgrec	093255
dc.rights.accessRights	open access	es_ES