Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage sco...

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Veröffentlicht in:	Nature communications Jg. 9; H. 1; S. 5229 - 15
Hauptverfasser:	Stephansen, Jens B., Olesen, Alexander N., Olsen, Mads, Ambati, Aditya, Leary, Eileen B., Moore, Hyatt E., Carrillo, Oscar, Lin, Ling, Han, Fang, Yan, Han, Sun, Yun L., Dauvilliers, Yves, Scholz, Sabine, Barateau, Lucie, Hogl, Birgit, Stefani, Ambra, Hong, Seung Chul, Kim, Tae Won, Pizza, Fabio, Plazzi, Giuseppe, Vandi, Stefano, Antelmi, Elena, Perrin, Dimitri, Kuna, Samuel T., Schweitzer, Paula K., Kushida, Clete, Peppard, Paul E., Sorensen, Helge B. D., Jennum, Poul, Mignot, Emmanuel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 06.12.2018 Nature Publishing Group Nature Portfolio
Schlagworte:	631/114/1305 631/378/1385 631/378/1385/519 692/617/375/1816 9/26 Automation Diagnosis Human health and pathology Humanities and Social Sciences Life Sciences multidisciplinary Narcolepsy Network analysis Neural networks Neurons and Cognition Probability distribution Science Science (multidisciplinary) Sleep Sleep disorders Technicians Tissue typing
ISSN:	2041-1723, 2041-1723
Online-Zugang:	Volltext
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