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Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

Author

Listed:
  • Jens B. Stephansen

    (Stanford University
    Technical University of Denmark)

  • Alexander N. Olesen

    (Stanford University
    Technical University of Denmark
    Rigshospitalet)

  • Mads Olsen

    (Stanford University
    Technical University of Denmark
    Rigshospitalet)

  • 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
    Université Montpellier 1)

  • Sabine Scholz

    (Gui-de-Chauliac Hospital
    Université Montpellier 1)

  • Lucie Barateau

    (Gui-de-Chauliac Hospital
    Université Montpellier 1)

  • 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
    IRCCS Istituto delle Scienze Neurologiche di Bologna)

  • Giuseppe Plazzi

    (University of Bologna
    IRCCS Istituto delle Scienze Neurologiche di Bologna)

  • Stefano Vandi

    (University of Bologna
    IRCCS Istituto delle Scienze Neurologiche di Bologna)

  • Elena Antelmi

    (University of Bologna
    IRCCS Istituto delle Scienze Neurologiche di 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)

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.

Suggested Citation

  • 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 Schol, 2018. "Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07229-3
    DOI: 10.1038/s41467-018-07229-3
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