Quiet sleep detection in preterm infants using deep convolutional neural networks

Objective. Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sl...

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Autores Principales: Ansari A.H., De Wel O., Lavanga M., Caicedo A., Dereymaeker A., Jansen K., Vervisch J., De Vos M., Naulaers G., Van Huffel S.
Formato: Artículo (Article)
Lenguaje:Inglés (English)
Publicado: Institute of Physics Publishing 2018
Materias:
EEG
Acceso en línea:https://repository.urosario.edu.co/handle/10336/22407
https://doi.org/10.1088/1741-2552/aadc1f
id ir-10336-22407
recordtype dspace
spelling ir-10336-224072022-05-02T12:37:14Z Quiet sleep detection in preterm infants using deep convolutional neural networks Ansari A.H. De Wel O. Lavanga M. Caicedo A. Dereymaeker A. Jansen K. Vervisch J. De Vos M. Naulaers G. Van Huffel S. Article Brain development Brain maturation Classification Clinical article Convolutional neural network Correlation analysis Electroencephalogram Electroencephalography Feature extraction Human Infant Machine learning Nerve cell differentiation Newborn care Prematurity Priority journal Receiver operating characteristic Sleep Sleep stage Algorithm Artificial neural network Automation Brain Female Male Newborn Physiology Prematurity Procedures Sleep Wakefulness Algorithms Automation Brain Electroencephalography Female Humans Male Neural Networks (Computer) Sleep Sleep Stages Wakefulness Convolutional neural network EEG Preterm neonate Sleep stage classification Newborn development and aging Premature Growth Infant Infant Objective. Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation. Approach. In this study, we designed and implemented an 18-layer convolutional neural network to discriminate quiet sleep from non-quiet sleep in preterm infants. The network is trained on 54 recordings from 13 preterm neonates and the performance is assessed on 43 recordings from 13 independent patients. All neonates had a normal neurodevelopmental outcome and the EEGs were recorded between 27 and 42 weeks postmenstrual age. Main results. The proposed network achieved an area under the mean and median ROC curve equal to 92% and 98%, respectively. Significance. Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants. © 2018 IOP Publishing Ltd. 2018 2020-05-25T23:56:22Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 17412560 https://repository.urosario.edu.co/handle/10336/22407 https://doi.org/10.1088/1741-2552/aadc1f eng info:eu-repo/semantics/openAccess application/pdf Institute of Physics Publishing instname:Universidad del Rosario
institution EdocUR - Universidad del Rosario
collection DSpace
language Inglés (English)
topic Article
Brain development
Brain maturation
Classification
Clinical article
Convolutional neural network
Correlation analysis
Electroencephalogram
Electroencephalography
Feature extraction
Human
Infant
Machine learning
Nerve cell differentiation
Newborn care
Prematurity
Priority journal
Receiver operating characteristic
Sleep
Sleep stage
Algorithm
Artificial neural network
Automation
Brain
Female
Male
Newborn
Physiology
Prematurity
Procedures
Sleep
Wakefulness
Algorithms
Automation
Brain
Electroencephalography
Female
Humans
Male
Neural Networks (Computer)
Sleep
Sleep Stages
Wakefulness
Convolutional neural network
EEG
Preterm neonate
Sleep stage classification
Newborn
development and aging
Premature
Growth
Infant
Infant
spellingShingle Article
Brain development
Brain maturation
Classification
Clinical article
Convolutional neural network
Correlation analysis
Electroencephalogram
Electroencephalography
Feature extraction
Human
Infant
Machine learning
Nerve cell differentiation
Newborn care
Prematurity
Priority journal
Receiver operating characteristic
Sleep
Sleep stage
Algorithm
Artificial neural network
Automation
Brain
Female
Male
Newborn
Physiology
Prematurity
Procedures
Sleep
Wakefulness
Algorithms
Automation
Brain
Electroencephalography
Female
Humans
Male
Neural Networks (Computer)
Sleep
Sleep Stages
Wakefulness
Convolutional neural network
EEG
Preterm neonate
Sleep stage classification
Newborn
development and aging
Premature
Growth
Infant
Infant
Ansari A.H.
De Wel O.
Lavanga M.
Caicedo A.
Dereymaeker A.
Jansen K.
Vervisch J.
De Vos M.
Naulaers G.
Van Huffel S.
Quiet sleep detection in preterm infants using deep convolutional neural networks
description Objective. Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation. Approach. In this study, we designed and implemented an 18-layer convolutional neural network to discriminate quiet sleep from non-quiet sleep in preterm infants. The network is trained on 54 recordings from 13 preterm neonates and the performance is assessed on 43 recordings from 13 independent patients. All neonates had a normal neurodevelopmental outcome and the EEGs were recorded between 27 and 42 weeks postmenstrual age. Main results. The proposed network achieved an area under the mean and median ROC curve equal to 92% and 98%, respectively. Significance. Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants. © 2018 IOP Publishing Ltd.
format Artículo (Article)
author Ansari A.H.
De Wel O.
Lavanga M.
Caicedo A.
Dereymaeker A.
Jansen K.
Vervisch J.
De Vos M.
Naulaers G.
Van Huffel S.
author_facet Ansari A.H.
De Wel O.
Lavanga M.
Caicedo A.
Dereymaeker A.
Jansen K.
Vervisch J.
De Vos M.
Naulaers G.
Van Huffel S.
author_sort Ansari A.H.
title Quiet sleep detection in preterm infants using deep convolutional neural networks
title_short Quiet sleep detection in preterm infants using deep convolutional neural networks
title_full Quiet sleep detection in preterm infants using deep convolutional neural networks
title_fullStr Quiet sleep detection in preterm infants using deep convolutional neural networks
title_full_unstemmed Quiet sleep detection in preterm infants using deep convolutional neural networks
title_sort quiet sleep detection in preterm infants using deep convolutional neural networks
publisher Institute of Physics Publishing
publishDate 2018
url https://repository.urosario.edu.co/handle/10336/22407
https://doi.org/10.1088/1741-2552/aadc1f
_version_ 1740172456468938752
score 12,131701