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1000 Titel
  • Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography
1000 Autor/in
  1. Dietz-Terjung, Sarah |
  2. Martin, Amelie Ricarda |
  3. Finnsson, Eysteinn |
  4. Ágústsson, Jón Skínir |
  5. Helgason, Snorri |
  6. Helgadóttir, Halla |
  7. Welsner, Matthias |
  8. Taube, Christian |
  9. Weinreich, Gerhard |
  10. Schöbel, Christoph |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-02-16
1000 Erschienen in
1000 Quellenangabe
  • 25(4):1945-1952
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s11325-021-02316-0 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590674/ |
1000 Publikationsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Purpose!#!In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep!##!Methods!#!Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0).!##!Results!#!We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen's kappa of 0.62 was found for this 3-class classification problem.!##!Conclusion!#!The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.
1000 Sacherschließung
lokal Aged, 80 and over [MeSH]
lokal Aged [MeSH]
lokal Sleep Apnea Syndromes/physiopathology [MeSH]
lokal RIP
lokal Artificial intelligence
lokal Recurrent neural network
lokal Neural Networks, Computer [MeSH]
lokal Sleep stage estimation
lokal Sleep Apnea Syndromes/diagnosis [MeSH]
lokal Male [MeSH]
lokal Plethysmography/standards [MeSH]
lokal Actigraphy/standards [MeSH]
lokal Adolescent [MeSH]
lokal Algorithms [MeSH]
lokal Female [MeSH]
lokal Adult [MeSH]
lokal Humans [MeSH]
lokal Severity of Illness Index [MeSH]
lokal Actigraphy
lokal Middle Aged [MeSH]
lokal Sleep Stages/physiology [MeSH]
lokal Sleep Breathing Physiology and Disorders • Original Article
lokal Polysomnography/standards [MeSH]
lokal Young Adult [MeSH]
lokal Proof of Concept Study [MeSH]
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0001-7907-6670|https://frl.publisso.de/adhoc/uri/TWFydGluLCBBbWVsaWUgUmljYXJkYQ==|https://frl.publisso.de/adhoc/uri/RmlubnNzb24sIEV5c3RlaW5u|https://frl.publisso.de/adhoc/uri/w4Fnw7pzdHNzb24sIErDs24gU2vDrW5pcg==|https://frl.publisso.de/adhoc/uri/SGVsZ2Fzb24sIFNub3JyaQ==|https://frl.publisso.de/adhoc/uri/SGVsZ2Fkw7N0dGlyLCBIYWxsYQ==|https://frl.publisso.de/adhoc/uri/V2Vsc25lciwgTWF0dGhpYXM=|https://frl.publisso.de/adhoc/uri/VGF1YmUsIENocmlzdGlhbg==|https://frl.publisso.de/adhoc/uri/V2VpbnJlaWNoLCBHZXJoYXJk|https://frl.publisso.de/adhoc/uri/U2Now7ZiZWwsIENocmlzdG9waA==
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1000 Erstellt am 2023-04-27T14:08:09.192+0200
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