Comput Math Methods Med - Comparison of different EHG feature selection methods for the detection of preterm labor.

Tópicos

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Resumo

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.

Resumo Limpo

numer type linear nonlinear featur extract electrohysterogram ehg order classifi labor pregnanc contract result number avail featur now larg goal studi reduc number featur select relev one use solv classif problem paper present three method featur subset select can appli choos best subset classifi labor pregnanc contract algorithm use jeffrey diverg jd distanc sequenti forward select sfs algorithm binari particl swarm optim bpso algorithm two last method base classifi test three type classifi method allow us identifi common featur relev contract classif

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