Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

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Standard

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. / 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.

I: Nature Communications, Bind 9, 5229, 2018.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Stephansen, JB, Olesen, AN, Olsen, M, Ambati, A, Leary, EB, Moore, HE, Carrillo, O, Lin, L, Han, F, Yan, H, Sun, YL, Dauvilliers, Y, Scholz, S, Barateau, L, Hogl, B, Stefani, A, Hong, SC, Kim, TW, Pizza, F, Plazzi, G, Vandi, S, Antelmi, E, Perrin, D, Kuna, ST, Schweitzer, PK, Kushida, C, Peppard, PE, Sorensen, HBD, Jennum, P & Mignot, E 2018, 'Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy', Nature Communications, bind 9, 5229. https://doi.org/10.1038/s41467-018-07229-3

APA

Stephansen, J. B., Olesen, A. N., Olsen, M., Ambati, A., Leary, E. B., Moore, H. E., Carrillo, O., Lin, L., Han, F., Yan, H., Sun, Y. L., Dauvilliers, Y., Scholz, S., Barateau, L., Hogl, B., Stefani, A., Hong, S. C., Kim, T. W., Pizza, F., ... Mignot, E. (2018). Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nature Communications, 9, [5229]. https://doi.org/10.1038/s41467-018-07229-3

Vancouver

Stephansen JB, Olesen AN, Olsen M, Ambati A, Leary EB, Moore HE o.a. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nature Communications. 2018;9. 5229. https://doi.org/10.1038/s41467-018-07229-3

Author

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. / Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. I: Nature Communications. 2018 ; Bind 9.

Bibtex

@article{934b49c578dc4812865ca95ccd48107d,
title = "Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy",
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.",
keywords = "Adolescent, Adult, Aged, Algorithms, Cohort Studies, Female, HLA-DQ beta-Chains/analysis, Humans, Male, Middle Aged, Narcolepsy/diagnosis, Neural Networks (Computer), Polysomnography, Sensitivity and Specificity, Sleep Stages/immunology, Young Adult",
author = "Stephansen, {Jens B} and Olesen, {Alexander N} and Mads Olsen and Aditya Ambati and Leary, {Eileen B} and Moore, {Hyatt E} and Oscar Carrillo and Ling Lin and Fang Han and Han Yan and Sun, {Yun L} and Yves Dauvilliers and Sabine Scholz and Lucie Barateau and Birgit Hogl and Ambra Stefani and Hong, {Seung Chul} and Kim, {Tae Won} and Fabio Pizza and Giuseppe Plazzi and Stefano Vandi and Elena Antelmi and Dimitri Perrin and Kuna, {Samuel T} and Schweitzer, {Paula K} and Clete Kushida and Peppard, {Paul E} and Sorensen, {Helge B D} and Poul Jennum and Emmanuel Mignot",
year = "2018",
doi = "10.1038/s41467-018-07229-3",
language = "English",
volume = "9",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

AU - Stephansen, Jens B

AU - Olesen, Alexander N

AU - Olsen, Mads

AU - Ambati, Aditya

AU - Leary, Eileen B

AU - Moore, Hyatt E

AU - Carrillo, Oscar

AU - Lin, Ling

AU - Han, Fang

AU - Yan, Han

AU - Sun, Yun L

AU - Dauvilliers, Yves

AU - Scholz, Sabine

AU - Barateau, Lucie

AU - Hogl, Birgit

AU - Stefani, Ambra

AU - Hong, Seung Chul

AU - Kim, Tae Won

AU - Pizza, Fabio

AU - Plazzi, Giuseppe

AU - Vandi, Stefano

AU - Antelmi, Elena

AU - Perrin, Dimitri

AU - Kuna, Samuel T

AU - Schweitzer, Paula K

AU - Kushida, Clete

AU - Peppard, Paul E

AU - Sorensen, Helge B D

AU - Jennum, Poul

AU - Mignot, Emmanuel

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Adolescent

KW - Adult

KW - Aged

KW - Algorithms

KW - Cohort Studies

KW - Female

KW - HLA-DQ beta-Chains/analysis

KW - Humans

KW - Male

KW - Middle Aged

KW - Narcolepsy/diagnosis

KW - Neural Networks (Computer)

KW - Polysomnography

KW - Sensitivity and Specificity

KW - Sleep Stages/immunology

KW - Young Adult

U2 - 10.1038/s41467-018-07229-3

DO - 10.1038/s41467-018-07229-3

M3 - Journal article

C2 - 30523329

VL - 9

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 5229

ER -

ID: 216471926