Sleep Staging with Convolutional Recurrent Neural Networks
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Author
Van Zaen, Jérôme
Braun, Fabian
Renevey, Philippe
Lemay, Mathieu
DOI
Abstract
The 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.
Publication Reference
CSEM Scientific and Technical Report 2021, p. 39
Year
2021