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Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor
Joint Authors
Marque, Catherine
Khalil, M.
Alamedine, D.
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-23
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Alamedine, D.& Khalil, M.& Marque, Catherine. 2013. Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-475399
Modern Language Association (MLA)
Alamedine, D.…[et al.]. Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-475399
American Medical Association (AMA)
Alamedine, D.& Khalil, M.& Marque, Catherine. Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-475399
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-475399