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. 1. Introduction Preterm birth, that is, birth before the 37th week of pregnancy, remains a major problem in obstetrics. Children born before term present a high risk of mortality as well as health and development problems [1]. According to the World Health Organization (WHO), preterm birth rates range between 5% and 12% of births and perinatal mortality occurs in 3% to 47% of these cases in even the most developed parts of the world [2]. Delivery occurs after the onset of regular and effective uterine contractions, which cause dilation of the cervix and expulsion of the fetus. A contraction of the uterine muscle occurs due to the generation of electrical activity in a given uterine cell that spreads to other, neighboring cells. The evolution of uterine contractions, from weak and ineffective during pregnancy to strong and effective during labor, is therefore related to an increase in cellular excitability to an increase in the synchronization of the entire uterus [3]. A primary aim of pregnancy is to maintain the well-being of both mother and fetus and to keep the latter in utero as long as needed for a healthy birth. During pregnancy, the monitoring of uterine contractility is crucial in order to differentiate normal contractions, which are ineffective, from those effective contractions which might cause early dilation of the cervix and induce preterm birth. Despite increased knowledge and understanding of the phenomena involved in the onset of preterm labor, the methods currently used in obstetrics are not precise enough for an early detection of preterm birth threats. We need a more reliable method for early detection and prevention of preterm birth threats. One of
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