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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 485684, 9 pages
Research Article

Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor

1CNRS UMR 7338, Biomécanique et Bio-Ingénierie, Université de Technologie de Compiègne, 60200 Compiègne, France
2Azm Platform for Research in Biotechnology and Its Applications, LASTRE Laboratory, Lebanese University, Tripoli, Lebanon

Received 29 June 2013; Revised 11 October 2013; Accepted 4 November 2013

Academic Editor: Brynjar Karlsson

Copyright © 2013 D. Alamedine et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.