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...

Full description

Autores:
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/22407
Acceso en línea:
https://doi.org/10.1088/1741-2552/aadc1f
https://repository.urosario.edu.co/handle/10336/22407
Palabra clave:
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
Rights
License
Abierto (Texto Completo)