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Comparative Study
. 2013:2013:485684.
doi: 10.1155/2013/485684. Epub 2013 Dec 23.

Comparison of different EHG feature selection methods for the detection of preterm labor

D Alamedine  1 , M Khalil  2 , C Marque  3
Affiliations
Comparative Study

Comparison of different EHG feature selection methods for the detection of preterm labor

D Alamedine et al. Comput Math Methods Med. 2013.

Abstract

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.

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Figures

Figure 1
Figure 1
(a) Position of the 16 monopolar electrodes [4]. (b) Vbi (i = 1–12) represent the 12 calculated bipolar signals.
Figure 2
Figure 2
Wavelet decomposition.
Figure 3
Figure 3
Color vector representing the distribution of distances between parameters.
Figure 4
Figure 4
Selection vector representing the best parameters for the discrimination between pregnancy and labor.
See this image and copyright information in PMC

References

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