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Authors: J. Foussier 1 ; P. Fonseca 2 ; X. Long 2 and S. Leonhardt 1

Affiliations: 1 RWTH Aachen University, Germany ; 2 Philips Research Eindhoven, Netherlands

ISBN: 978-989-8565-35-8

Keyword(s): Sleep Monitoring, Sleep Staging, Feature Selection, Linear Discriminant Classification, Unobtrusive Monitoring, Cohen’s Kappa, Spearman’s Ranked-order Correlation.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning ; Pattern Recognition, Clustering and Classification

Abstract: This paper describes an automatic feature selection algorithm integrated into a classification framework developed to discriminate between sleep and wake states during the night. The feature selection algorithm proposed in this paper uses the Mahalanobis distance and the Spearman’s ranked-order correlation as selection criteria to restrict search in a large feature space. The algorithm was tested using a leave-one-subject-out cross-validation procedure on 15 single-night PSG recordings of healthy sleepers and then compared to the results of a standard Sequential Forward Search (SFS) algorithm. It achieved comparable performance in terms of Cohen’s kappa (k = 0.62) and the Area under the Precision-Recall curve (AUCPR = 0.59), but gave a significant computational time improvement by a factor of nearly 10. The feature selection procedure, applied on each iteration of the cross-validation, was found to be stable, consistently selecting a similar list of features. It selected an average of 10.33 features per iteration, nearly half of the 21 features selected by SFS. In addition, learning curves show that the training and testing performances converge faster than for SFS and that the final training-testing performance difference is smaller, suggesting that the new algorithm is more adequate for data sets with a small number of subjects. (More)

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Paper citation in several formats:
Foussier, J.; Fonseca, P.; Long, X. and Leonhardt, S. (2013). Automatic Feature Selection for Sleep/Wake Classification with Small Data Sets.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013) ISBN 978-989-8565-35-8, pages 178-184. DOI: 10.5220/0004245401780184

@conference{bioinformatics13,
author={J. Foussier. and P. Fonseca. and X. Long. and S. Leonhardt.},
title={Automatic Feature Selection for Sleep/Wake Classification with Small Data Sets},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)},
year={2013},
pages={178-184},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004245401780184},
isbn={978-989-8565-35-8},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)
TI - Automatic Feature Selection for Sleep/Wake Classification with Small Data Sets
SN - 978-989-8565-35-8
AU - Foussier, J.
AU - Fonseca, P.
AU - Long, X.
AU - Leonhardt, S.
PY - 2013
SP - 178
EP - 184
DO - 10.5220/0004245401780184

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