Analysis of ventricular fibrillation signals using feature selection methods

Feature selection methods in machine learning models are a powerful tool to knowledge extraction. In this work they are used to analyse the intrinsic modifications of cardiac response during ventricular fibrillation due to physical exercise. The data used are two sets of registers from isolated rabbit hearts: control (G1: without physical training), and trained (G2). Four parameters were extracted (dominant frequency, normalized energy, regularity index and number of occurrences). From them, 18 features were extracted. This work analyses the relevance of each feature to classify the records in G1 and G2 using Logistic Regression, Multilayer Perceptron and Extreme Learning Machine. Three feature selection methods are presented: one based on the output variation, other on the classification results and, finally, another method based in the variation in ROC curve. Although we obtained different sorting of features for each used classifier, the features related to the mean value and standard deviation of dominant frequency and regularity index were the most relevant, stating that the modifications in VF response produced by physical exercise are related to the cardiac activation rate, as to the regularity of that activation.