Sleep Staging with Convolutional Recurrent Neural Networks

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dc.contributor.authorVan Zaen, Jérôme
dc.contributor.authorBraun, Fabian
dc.contributor.authorRenevey, Philippe
dc.contributor.authorLemay, Mathieu
dc.date.accessioned2025-11-11T15:37:36Z
dc.date.available2025-11-11T15:37:36Z
dc.date.issued2021
dc.description.abstractThe correct identification of sleep stages is of the utmost importance in the diagnosis of sleep disorders. The gold standard for sleep staging is polysomnography which is laborious and expensive as it requires several sensors and manual annotation by a trained specialist. To mitigate these issues, a neural network architecture was developed to classify sleep stages from a single photoplethysmogram signal. Such an architecture could be fed data collected with a wearable sensor allowing for the automatic and unobtrusive estimation of sleep stages over several nights in home settings.
dc.identifier.citationCSEM Scientific and Technical Report 2021, p. 39
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1769
dc.titleSleep Staging with Convolutional Recurrent Neural Networks
dc.typeCSEM Report
dc.type.csemdivisionsBU-D
dc.type.csemresearchareasDigital Health
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