Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

Objective: We investigate the design of deep recurrent neural networks for detecting sleep stages from single channel EEG signals recorded at home by non-expert users. We report the effect of data set size, architecture choices, regularization, and personalization on the classification performance.M...

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Bibliographic Details
Main Authors: Erik Bresch, Ulf Großekathöfer, Gary Garcia-Molina
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-10-01
Series:Frontiers in Computational Neuroscience
Subjects:
deep learning
recurrent networks
EEG
sleep staging
hypnogram
Online Access:https://www.frontiersin.org/article/10.3389/fncom.2018.00085/full

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https://www.frontiersin.org/article/10.3389/fncom.2018.00085/full

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