Classification of EGG Signals Combining Neural Networks and Independent Component Analysis - Santos, Hallan; Montesco, Carlos A. E.; C. Júnior, Methanias (Proceedings of the Brazilian Symposium on Computing Applied to Health (SBCAS), 2014)
Identify digestive traits of people through Electrogastrography (EGG) is important because it is usually an economic, non-invasive and less bother than the traditional Endoscopy procedure. This research evaluates the behavior of artificial neural networks learning on the components extracted by Independent Component Analysis (ICA) algorithms. An experiment with statistical analysis whose goal was to present the relationship between the viewing of neutral, negative or positive images and digestive reactions was performed. The results showed that extract only the stomach signal component may reduce the error rate of learning of the neural network compared with experiment.