Enhancing feature extraction for VF detection using data mining techniques
Previous studies developed by the authors proposed VF detection algorithms, including VT discrimination, based on time-frequency distributions. However due to the large number of parameters extracted from the distributions, efficient schemes for parameter selection and significance estimation are needed. This study proposes a combined strategy of classical and modern techniques for the selection of parameters to develop improved VF detection algorithms. We show how exhaustive exploration of the input space using data mining techniques simplifies and improves the solution and reduces the computational cost of detection algorithms. Jointly with classical selection techniques (correlation, Wilks' Lambda, statistical significance), other approaches are used (PCA, SOM-Ward and CART). We show that better results are achieved using less number of parameters than previous VF detection algorithms.