CNRS UMR 7338, Biomécanique et Bio-Ingénierie, Université de Technologie de Compiègne, 60200 Compiègne, France ; Azm Platform for Research in Biotechnology and Its Applications, LASTRE Laboratory, Lebanese University, Tripoli, Lebanon.
Azm Platform for Research in Biotechnology and Its Applications, LASTRE Laboratory, Lebanese University, Tripoli, Lebanon.
Comput Math Methods Med. 2013;2013:485684. doi: 10.1155/2013/485684. Epub 2013 Dec 23.
Numerous types of linear and nonlinear features have been extracted from the electrohysterogram (EHG) in order to classify labor and pregnancy contractions. As a result, the number of available features is now very large. The goal of this study is to reduce the number of features by selecting only the relevant ones which are useful for solving the classification problem. This paper presents three methods for feature subset selection that can be applied to choose the best subsets for classifying labor and pregnancy contractions: an algorithm using the Jeffrey divergence (JD) distance, a sequential forward selection (SFS) algorithm, and a binary particle swarm optimization (BPSO) algorithm. The two last methods are based on a classifier and were tested with three types of classifiers. These methods have allowed us to identify common features which are relevant for contraction classification.
为了对分娩和妊娠宫缩进行分类，已经从电子宫图（EHG）中提取了许多类型的线性和非线性特征。因此，现在可用的特征数量非常多。本研究的目的是通过仅选择对解决分类问题有用的相关特征来减少特征数量。本文提出了三种特征子集选择方法，可用于选择用于分类分娩和妊娠宫缩的最佳子集：使用杰弗里散度（JD）距离的算法、顺序前向选择（SFS）算法和二进制粒子群优化（BPSO）算法。后两种方法基于分类器，并使用三种类型的分类器进行了测试。这些方法使我们能够识别出与宫缩分类相关的常见特征。
