Probabilistic Sleep Staging in MSLTs across Hypersomnia Disorders.

hypersomnia machine learning microsleep mslt narcolepsy

Journal

Sleep
ISSN: 1550-9109
Titre abrégé: Sleep
Pays: United States
ID NLM: 7809084

Informations de publication

Date de publication:
11 Oct 2024
Historique:
received: 01 03 2024
medline: 11 10 2024
pubmed: 11 10 2024
entrez: 11 10 2024
Statut: aheadofprint

Résumé

This study aimed to identify novel markers of narcolepsy type 1 (NT1) using between-nap opportunity periods ('Lights On') and in-nap opportunity periods ('Lights Off') features of Multiple Sleep Latency Test (MSLT) recordings. We hypothesized that NT1 could be identified both from sleep-wake instability and patterns of sleepiness during wakefulness. Further, we explored if MSLTs from NT1 and narcolepsy type 2 (NT2) patients could be distinguished despite having the same diagnostic thresholds. We analyzed 'Lights On' and 'Lights Off' periods of the MSLT, extracting 163 features describing sleepiness, microsleep, and sleep stage mixing using data from 177 patients with NT1, NT2, Idiopathic Hypersomnia (IH), and Subjective Hypersomnia (sH) from three sleep centers. These features were based on automated probabilistic sleep staging, also denoted as hypnodensities, using U-Sleep. Hypersomnias were differentiated using either or both features from 'Lights On' and 'Lights Off'. Patients with NT1 could be distinguished from NT2, IH, and sH using features solely from 'Lights On' periods with a sensitivity of 0.76 and specificity of 0.71. When using features from all periods of the MSLT, NT1 was distinguished from NT2 alone with a sensitivity of 0.77 and a specificity of 0.84. The findings of this study demonstrate microsleeps and sleep stage mixing as potential markers of the sleep attacks and unstable sleep-wake states common in NT1. Further, NT1 and NT2 could be frequently distinguished using 'Lights Off' features.

Identifiants

pubmed: 39392922
pii: 7818427
doi: 10.1093/sleep/zsae241
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.

Auteurs

Louise Hjuler Andersen (L)

Department of Health Technology, Technical University of Denmark, Kongens Lyngby,Denmark.
Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark.
Stanford Center for Sleep Sciences and Medicine, Stanford University, CA, USA.

Andreas Brink-Kjaer (A)

Department of Health Technology, Technical University of Denmark, Kongens Lyngby,Denmark.
Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark.

Oliver Sum-Ping (O)

Stanford Center for Sleep Sciences and Medicine, Stanford University, CA, USA.

Fabio Pizza (F)

IRCCS Istituto delle Scienze Neurologiche di Bologna, Italy.
Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.

Francesco Biscarini (F)

IRCCS Istituto delle Scienze Neurologiche di Bologna, Italy.
Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.

Niels Christian Haubjerg Østerby (NC)

Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark.

Emmanuel Mignot (E)

Stanford Center for Sleep Sciences and Medicine, Stanford University, CA, USA.

Giuseppe Plazzi (G)

IRCCS Istituto delle Scienze Neurologiche di Bologna, Italy.
Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio-Emilia, Italy.

Poul J Jennum (PJ)

Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark.

Classifications MeSH