Vista Equipo: Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation

Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation

This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analys...

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Detalles Bibliográficos
Autores Principales:	Lavanga, M., De Wel, O, Caicedo, A, Heremans, E, Jansen, K, Dereymaeker, A, Naulaers, G, Van Huffel, S
Formato:	Capítulo de libro (Book Chapter)
Lenguaje:	Inglés (English)
Publicado:	IEEE 2017
Materias:	Fractals Pediatrics Sleep Electroencephalography Entropy Training Brain modeling
Acceso en línea:	https://repository.urosario.edu.co/handle/10336/28923 https://doi.org/10.1109/EMBC.2017.8037246

id	ir-10336-28923
recordtype	dspace
spelling	ir-10336-289232020-08-28T15:50:40Z Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation Detección automática del sueño silencioso basada en la multifractalidad en recién nacidos prematuros: efectos de la maduración Lavanga, M. De Wel, O Caicedo, A Heremans, E Jansen, K Dereymaeker, A Naulaers, G Van Huffel, S Fractals Pediatrics Sleep Electroencephalography Entropy Training Brain modeling This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age. In particular, from the developed classifiers, the lowest area under the curve (AUC) has been obtained for EEG recordings at very young age (? 31 weeks post-menstrual age), and the maximum at full-term age (? 37 weeks post-menstrual age). The improvement in classification performances can be due to a change in the multifractality properties of neonatal EEG during the maturation of the infant, which makes the EEG sleep stages more distinguishable. 2017-09-14 2020-08-28T15:50:07Z info:eu-repo/semantics/bookPart info:eu-repo/semantics/publishedVersion ISBN: 978-1-5090-2810-8 EISBN: 978-1-5090-2809-2 https://repository.urosario.edu.co/handle/10336/28923 https://doi.org/10.1109/EMBC.2017.8037246 eng info:eu-repo/semantics/restrictedAccess application/pdf IEEE 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
institution	EdocUR - Universidad del Rosario
collection	DSpace
language	Inglés (English)
topic	Fractals Pediatrics Sleep Electroencephalography Entropy Training Brain modeling
spellingShingle	Fractals Pediatrics Sleep Electroencephalography Entropy Training Brain modeling Lavanga, M. De Wel, O Caicedo, A Heremans, E Jansen, K Dereymaeker, A Naulaers, G Van Huffel, S Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation
description	This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age. In particular, from the developed classifiers, the lowest area under the curve (AUC) has been obtained for EEG recordings at very young age (? 31 weeks post-menstrual age), and the maximum at full-term age (? 37 weeks post-menstrual age). The improvement in classification performances can be due to a change in the multifractality properties of neonatal EEG during the maturation of the infant, which makes the EEG sleep stages more distinguishable.
format	Capítulo de libro (Book Chapter)
author	Lavanga, M. De Wel, O Caicedo, A Heremans, E Jansen, K Dereymaeker, A Naulaers, G Van Huffel, S
author_facet	Lavanga, M. De Wel, O Caicedo, A Heremans, E Jansen, K Dereymaeker, A Naulaers, G Van Huffel, S
author_sort	Lavanga, M.
title	Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation
title_short	Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation
title_full	Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation
title_fullStr	Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation
title_full_unstemmed	Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation
title_sort	automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation
publisher	IEEE
publishDate	2017
url	https://repository.urosario.edu.co/handle/10336/28923 https://doi.org/10.1109/EMBC.2017.8037246
_version_	1676708385212334080
score	12,111491

Automatic quiet sleep detection based on multifractality in preterm neonates: effects of maturation

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