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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Detalhes bibliográficos
Publicado no:Sleep
Principais autores: Korkalainen, Henri, Aakko, Juhani, Duce, Brett, Kainulainen, Samu, Leino, Akseli, Nikkonen, Sami, Afara, Isaac O, Myllymaa, Sami, Töyräs, Juha, Leppänen, Timo
Formato: Artigo
Idioma:Inglês
Publicado em: Oxford University Press 2020
Assuntos:
Big Data Approaches to Sleep and Circadian Science
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7658638/
https://ncbi.nlm.nih.gov/pubmed/32436942
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/sleep/zsaa098
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