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 and Emmanuel Mignot ()
Additional contact information
Jens B. Stephansen: Stanford University
Alexander N. Olesen: Stanford University
Mads Olsen: Stanford University
Aditya Ambati: Stanford University
Eileen B. Leary: Stanford University
Hyatt E. Moore: Stanford University
Oscar Carrillo: Stanford University
Ling Lin: Stanford University
Fang Han: Peking University People’s Hospital
Han Yan: Peking University People’s Hospital
Yun L. Sun: Peking University People’s Hospital
Yves Dauvilliers: Gui-de-Chauliac Hospital
Sabine Scholz: Gui-de-Chauliac Hospital
Lucie Barateau: Gui-de-Chauliac Hospital
Birgit Hogl: Innsbruck Medical University
Ambra Stefani: Innsbruck Medical University
Seung Chul Hong: The Catholic University of Korea
Tae Won Kim: The Catholic University of Korea
Fabio Pizza: University of Bologna
Giuseppe Plazzi: University of Bologna
Stefano Vandi: University of Bologna
Elena Antelmi: University of Bologna
Dimitri Perrin: Queensland University of Technology
Samuel T. Kuna: University of Pennsylvania
Paula K. Schweitzer: St. Luke’s Hospital
Clete Kushida: Stanford University
Paul E. Peppard: University of Wisconsin-Madison
Helge B. D. Sorensen: Technical University of Denmark
Poul Jennum: Rigshospitalet
Emmanuel Mignot: Stanford University

Nature Communications, 2018, vol. 9, issue 1, 1-15

Abstract: Abstract 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 scoring, producing a hypnodensity graph—a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-018-07229-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07229-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-018-07229-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().