Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation.
Comput Biol Med
; 45: 1-7, 2014 Feb.
Article
de En
| MEDLINE
| ID: mdl-24480157
ABSTRACT
This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better 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.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Conditionnement physique d'animal
/
Fibrillation ventriculaire
/
Traitement du signal assisté par ordinateur
/
Aptitude physique
Limites:
Animals
Langue:
En
Journal:
Comput Biol Med
Année:
2014
Type de document:
Article