Nature Communications (Dec 2018)
Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy
- Jens B. Stephansen,
- Alexander N. Olesen,
- Mads Olsen,
- Aditya Ambati,
- Eileen B. Leary,
- Hyatt E. Moore,
- Oscar Carrillo,
- Ling Lin,
- Fang Han,
- Han Yan,
- Yun L. Sun,
- Yves Dauvilliers,
- Sabine Scholz,
- Lucie Barateau,
- Birgit Hogl,
- Ambra Stefani,
- Seung Chul Hong,
- Tae Won Kim,
- Fabio Pizza,
- Giuseppe Plazzi,
- Stefano Vandi,
- Elena Antelmi,
- Dimitri Perrin,
- Samuel T. Kuna,
- Paula K. Schweitzer,
- Clete Kushida,
- Paul E. Peppard,
- Helge B. D. Sorensen,
- Poul Jennum,
- Emmanuel Mignot
Affiliations
- Jens B. Stephansen
- Center for Sleep Science and Medicine, Stanford University
- Alexander N. Olesen
- Center for Sleep Science and Medicine, Stanford University
- Mads Olsen
- Center for Sleep Science and Medicine, Stanford University
- Aditya Ambati
- Center for Sleep Science and Medicine, Stanford University
- Eileen B. Leary
- Center for Sleep Science and Medicine, Stanford University
- Hyatt E. Moore
- Center for Sleep Science and Medicine, Stanford University
- Oscar Carrillo
- Center for Sleep Science and Medicine, Stanford University
- Ling Lin
- Center for Sleep Science and Medicine, Stanford University
- Fang Han
- Department of Pulmonary Medicine, Peking University People’s Hospital
- Han Yan
- Department of Pulmonary Medicine, Peking University People’s Hospital
- Yun L. Sun
- Department of Pulmonary Medicine, Peking University People’s Hospital
- Yves Dauvilliers
- Sleep-Wake Disorders Center, Department of Neurology, Gui-de-Chauliac Hospital
- Sabine Scholz
- Sleep-Wake Disorders Center, Department of Neurology, Gui-de-Chauliac Hospital
- Lucie Barateau
- Sleep-Wake Disorders Center, Department of Neurology, Gui-de-Chauliac Hospital
- Birgit Hogl
- Department of Neurology, Innsbruck Medical University
- Ambra Stefani
- Department of Neurology, Innsbruck Medical University
- Seung Chul Hong
- Department of Psychiatry, St. Vincent’s Hospital, The Catholic University of Korea
- Tae Won Kim
- Department of Psychiatry, St. Vincent’s Hospital, The Catholic University of Korea
- Fabio Pizza
- Department of Biomedical and Neuromotor Sciences, University of Bologna
- Giuseppe Plazzi
- Department of Biomedical and Neuromotor Sciences, University of Bologna
- Stefano Vandi
- Department of Biomedical and Neuromotor Sciences, University of Bologna
- Elena Antelmi
- Department of Biomedical and Neuromotor Sciences, University of Bologna
- Dimitri Perrin
- School of Electrical Engineering and Computer Science, Queensland University of Technology
- Samuel T. Kuna
- Department of Medicine and Center for Sleep and Circadian Neurobiology, University of Pennsylvania
- Paula K. Schweitzer
- Sleep Medicine and Research Center, St. Luke’s Hospital
- Clete Kushida
- Center for Sleep Science and Medicine, Stanford University
- Paul E. Peppard
- Department of Population Health Sciences, University of Wisconsin-Madison
- Helge B. D. Sorensen
- Department of Electrical Engineering, Technical University of Denmark
- Poul Jennum
- Danish Center for Sleep Medicine, Rigshospitalet
- Emmanuel Mignot
- Center for Sleep Science and Medicine, Stanford University
- DOI
- https://doi.org/10.1038/s41467-018-07229-3
- Journal volume & issue
-
Vol. 9,
no. 1
pp. 1 – 15
Abstract
The diagnosis of sleep disorders such as narcolepsy and insomnia currently requires experts to interpret sleep recordings (polysomnography). Here, the authors introduce a neural network analysis method for polysomnography that could reduce time spent in sleep clinics and automate narcolepsy diagnosis.
