A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea-Reference-Cited by-同舟云学术

A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea

Published:2023-07-24 Issue: Volume: Page:
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Container-title:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Vaquerizo-Villar Fernando¹,Álvarez Daniel¹,Gutiérrez-Tobal Gonzalo C.¹,Del Campo Félix¹,Gozal David²,Kheirandish-Gozal Leila²,Penzel Thomas³,Hornero Roberto¹

Affiliation:

1. Universidad de Valladolid and CIBER-BBN (ISCIII),Biomedical Engineering Group,Spain

2. The University of Missouri School of Medicine,Department of Child Health,Columbia,Missouri,USA

3. Charité-Universitätsmedizin,Interdisciplinary Center of Sleep Medicine,Berlin,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10339936/10339939/10341100.pdf?arnumber=10341100

Reference18 articles.

1. International Classification of Sleep Disorders-Third Edition

2. The AASM Manual for the Scoring of Sleep and Associated Events;Berry;Am. Acad. Sleep Med.,2018

3. Oximetry use in obstructive sleep apnea

4. Performance of an Automated Polysomnography Scoring System Versus Computer-Assisted Manual Scoring

5. A review of automated sleep stage scoring based on physiological signals for the new millennia

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2. Accurate and Interpretable Deep Learning Model for Sleep Staging in Children with Sleep Apnea from Pulse Oximetry;IFMBE Proceedings;2024