Comparison of artificial neural networks an support vector machines for feature selection in electrogastrography signal processing

The paper describes a feature selection process applied to electrogastrogram (EGG) processing. The data set is formed by 42 EGG records from functional dyspeptic (FD) patients and 22 from healthy controls. A wrapper configuration classifier was implemented to discriminate between both classes. The aim of this work is to compare artificial neural networks (ANN) and support vector machines (SVM) when acting as fitness functions of a genetic algorithm (GA) that performs a feature selection process over some features extracted from the EGG signals. These features correspond to those that literature shows to be the most used in EGG analysis. The results show that the SVM classifier is faster, requires less memory and reached the same performance (86% of exactitude) than the ANN classifier when acting as the fitness function for the GA.