6533b7d8fe1ef96bd126aece
RESEARCH PRODUCT
Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation
Antonio J. SerranoLuis Such-miquelJuan CaravacaM. BatallerEmilio Soria-olivasJuan GuerreroJoan Vila-francéssubject
MaleComputer scienceHealth InformaticsPhysical exerciseFeature selectionMachine learningcomputer.software_genreElectrocardiographyKnowledge extractionArtificial IntelligencePhysical Conditioning AnimalmedicineAnimalsExtreme learning machinebusiness.industryDimensionality reductionWork (physics)Signal Processing Computer-Assistedmedicine.diseaseComputer Science ApplicationsCor MalaltiesPhysical FitnessMultilayer perceptronVentricular fibrillationVentricular FibrillationEnginyeria biomèdicaArtificial intelligenceRabbitsbusinesscomputerdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-01-01 |