Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

BACKGROUND. Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnogr...

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Detalles Bibliográficos
Autores principales: Yuan, Hang, Plekhanova, Tatiana, Walmsley, Rosemary, Reynolds, Amy C., Maddison, Kathleen J., Bucan, Maja, Gehrman, Philip, Rowlands, Alex, Ray, David W., Bennett, Derrick, McVeigh, Joanne, Straker, Leon, Eastwood, Peter, Kyle, Simon D., Doherty, Aiden
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350137/
https://www.ncbi.nlm.nih.gov/pubmed/37461532
http://dx.doi.org/10.1101/2023.07.07.23292251

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350137/
https://www.ncbi.nlm.nih.gov/pubmed/37461532
http://dx.doi.org/10.1101/2023.07.07.23292251

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