Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea

STUDY OBJECTIVES: Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiati...

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Detalles Bibliográficos
Autores principales: Korkalainen, Henri, Aakko, Juhani, Duce, Brett, Kainulainen, Samu, Leino, Akseli, Nikkonen, Sami, Afara, Isaac O, Myllymaa, Sami, Töyräs, Juha, Leppänen, Timo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Big Data Approaches to Sleep and Circadian Science
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658638/
https://www.ncbi.nlm.nih.gov/pubmed/32436942
http://dx.doi.org/10.1093/sleep/zsaa098

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658638/
https://www.ncbi.nlm.nih.gov/pubmed/32436942
http://dx.doi.org/10.1093/sleep/zsaa098

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