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

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Caravaca Moreno, Juan; Soria Olivas, Emilio; Bataller Mompean, Manuel; Serrano López, Antonio José; Such Miquel, Luis; Vila Francés, Joan; Guerrero Martínez, Juan Francisco
This document is a artículoDate2014

Este documento está disponible también en : http://hdl.handle.net/10550/42558
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.

    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
http://dx.doi.org/10.1016/j.compbiomed.2013.11.008

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