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.
Fernando Vaquerizo-VillarDaniel ÁlvarezGonzalo C. Gutiérrez-TobalFélix del CampoDavid GozalLeila Kheirandish-GozalThomas PenzelRoberto HorneroPublished in: EMBC (2023)
Keyphrases
- deep learning
- recurrent neural networks
- sleep apnea
- sleep stage
- obstructive sleep apnea
- unsupervised learning
- unsupervised feature learning
- machine learning
- feed forward
- physiological parameters
- neural network
- restricted boltzmann machine
- echo state networks
- automatic analysis
- recurrent networks
- artificial neural networks
- mental models
- weakly supervised
- hidden layer
- deep belief networks
- question answering
- image processing