Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation.
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.
Digital Signal Processing Group, ETSE, Universitat de València, Avda Universitat S/N, Burjassot, València 46100, Spain. Electronic address: caravaca.juan@gmail.com.
Juan
J
Caravaca
Intelligent Data Analysis Laboratory, ETSE, Universitat de València, Avda Universitat S/N, Burjassot, València 46100, Spain.
Emilio
E
Soria-Olivas
Digital Signal Processing Group, ETSE, Universitat de València, Avda Universitat S/N, Burjassot, València 46100, Spain.
Manuel
M
Bataller
Intelligent Data Analysis Laboratory, ETSE, Universitat de València, Avda Universitat S/N, Burjassot, València 46100, Spain.
Antonio J
AJ
Serrano
Department of Physiotherapy, Universitat de València and INCLIVA, València, Spain.
Luis
L
Such-Miquel
Intelligent Data Analysis Laboratory, ETSE, Universitat de València, Avda Universitat S/N, Burjassot, València 46100, Spain.
Joan
J
Vila-Francés
Digital Signal Processing Group, ETSE, Universitat de València, Avda Universitat S/N, Burjassot, València 46100, Spain.
Juan F
JF
Guerrero
D000818:Animals; D001185:Artificial Intelligence; D004562:Electrocardiography / Q000145:classification; D008297:Male; D010805:Physical Conditioning, Animal / Q000502:physiology*; D010809:Physical Fitness / Q000502:physiology*; D011817:Rabbits; D012815:Signal Processing, Computer-Assisted; D014693:Ventricular Fibrillation / Q000145:classification / Q000503:physiopathology*
Classification; Extreme learning machine; Knowledge extraction; Logistic regression; Machine learning; Multilayer perceptron; Physical exercise; Ventricular fibrillation