Comparison of different EHG feature selection methods for the detection of preterm labor.
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.
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.
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Alamedine
Azm Platform for Research in Biotechnology and Its Applications, LASTRE Laboratory, Lebanese University, Tripoli, Lebanon.
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Khalil
CNRS UMR 7338, Biomécanique et Bio-Ingénierie, Université de Technologie de Compiègne, 60200 Compiègne, France.
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Marque
D000465:Algorithms; D004566:Electrodes; D004568:Electrodiagnosis / Q000379:methods*; D005260:Female; D005323:Fetal Monitoring / Q000379:methods*; D005865:Gestational Age; D006801:Humans; D016014:Linear Models; D016011:Normal Distribution; D007752:Obstetric Labor, Premature / Q000175:diagnosis* / Q000473:pathology*; D011247:Pregnancy; D047928:Premature Birth; D012815:Signal Processing, Computer-Assisted; D014599:Uterus / Q000503:physiopathology