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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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Detalles Bibliográficos
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:
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
Procedures
Wakefulness
Algorithms
Humans
Neural Networks (Computer)
Sleep Stages
EEG
Preterm neonate
Sleep stage classification
development and aging
Premature
Growth
Acceso en línea:https://repository.urosario.edu.co/handle/10336/22407
https://doi.org/10.1088/1741-2552/aadc1f

Internet

https://repository.urosario.edu.co/handle/10336/22407
https://doi.org/10.1088/1741-2552/aadc1f

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