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 a...

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Bibliographic Details
Published in:2010 Annual International Conference of the IEEE Engineering in Medicine and Biology 2010-01, p.2774-2777
Main Authors: Curilem, M, Chacón, Max, Acuña, G, Ulloa, S, Pardo, C, Defilippi, C, Madrid, Ana María
Format: Article
Language:English
Subjects:
Artificial neural networks
Biological cells
Classification algorithms
Feature extraction
Genetic algorithms
Support vector machines
Training
Online Access:Request full text
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Summary: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.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2010.5626362