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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2018
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Acceso en línea: | https://repository.urosario.edu.co/handle/10336/22407 https://doi.org/10.1088/1741-2552/aadc1f |
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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 |
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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 |