Curilem Millaray, Chacon Max, Acuna Gonzalo, Ulloa Sebastian, Pardo Carlos, Defilippi Carlos, Madrid Ana Maria
Electrical Engineering Department, Universdad de la Frontera, UFRO, Av. Francisco Salazar 01145, Temuco, CHILE.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2774-7. doi: 10.1109/IEMBS.2010.5626362.
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.
本文描述了一种应用于胃电图（EGG）处理的特征选择过程。数据集由42例功能性消化不良（FD）患者的EGG记录和22例健康对照者的记录组成。实现了一个包装器配置分类器来区分这两类。这项工作的目的是比较人工神经网络（ANN）和支持向量机（SVM）在作为遗传算法（GA）的适应度函数时的情况，该遗传算法对从EGG信号中提取的一些特征进行特征选择过程。这些特征与文献中显示的在EGG分析中最常用的特征相对应。结果表明，当作为GA的适应度函数时，SVM分类器速度更快，所需内存更少，并且与ANN分类器达到了相同的性能（准确率86%）。
