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dc.date.accessioned2021-02-03T20:53:35Z
dc.date.available2021-02-03T20:53:35Z
dc.date.created2021-01-20T16:23:57Z
dc.date.issued2020
dc.identifier.citationKristiansen, Stein Traaen, Gunn Marit Øverland, Britt Plagemann, Thomas Peter Gullestad, Lars Akre, Harriet Nikolaidis, Konstantinos Aakerøy, Lars Hunt, Tove Elizabeth Frances Loennechen, Jan Pål Steinshamn, Sigurd Loe Bendz, Christina Anfinsen, Ole-Gunnar Goebel, Vera Hermine . Comparing manual and automatic scoring of sleep monitoring data from portable polygraphy. Journal of Sleep Research. 2020
dc.identifier.urihttp://hdl.handle.net/10852/82872
dc.description.abstractWe used sleep monitoring data from a study that investigated the prevalence, characteristics, risk factors and type of sleep apnea (SA) in 579 patients with paroxysmal atrial fibrillation. Most patients were screened for two nights, resulting in 1,043 sleep recordings that each contained data from one night. SA was diagnosed using the Nox T3 portable sleep monitor. An experienced sleep specialist scored the recordings manually using Noxturnal software. A total of 157 women (27%) and 422 men (73%) were examined; 477 (82.7%) had an apnea–hypopnea index (AHI) ≥ 5/hr, whereas moderate to severe SA (AHI ≥ 15/hr) was diagnosed in 243 patients (42.1%). The AHI derived from automatic and manual scoring showed a good agreement (Pearson's r coefficient of 0.96). The median difference in AHI was very small (i.e., 0.72 [mean difference, 1.06]), but was statistically significant (p < .0001). Automatic scoring classified sleep recordings with more than 90% accuracy into SA categories of mild (AHI ≥ 5/hr), moderate (AHI ≥ 15/hr) and severe (AHI ≥ 30/hr). We found a minor (11%–21%) mis‐estimation of the number of recordings right above and below the boundary separating mild and moderate SA. The accuracy of automatic scoring differed from recording to recording, especially regarding the sensitivity of detecting disrupted breathing events. We found low to moderate agreement for the duration of disrupted breathing events (r = .53), for which the automatic scoring led to a statistically significant overestimation by 5.22 s (p < .0001).
dc.languageEN
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleComparing manual and automatic scoring of sleep monitoring data from portable polygraphy
dc.typeJournal article
dc.creator.authorKristiansen, Stein
dc.creator.authorTraaen, Gunn Marit
dc.creator.authorØverland, Britt
dc.creator.authorPlagemann, Thomas Peter
dc.creator.authorGullestad, Lars
dc.creator.authorAkre, Harriet
dc.creator.authorNikolaidis, Konstantinos
dc.creator.authorAakerøy, Lars
dc.creator.authorHunt, Tove Elizabeth Frances
dc.creator.authorLoennechen, Jan Pål
dc.creator.authorSteinshamn, Sigurd Loe
dc.creator.authorBendz, Christina
dc.creator.authorAnfinsen, Ole-Gunnar
dc.creator.authorGoebel, Vera Hermine
cristin.unitcode185,15,5,75
cristin.unitnameDIS Digital infrastruktur og sikkerhet
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin1875828
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal of Sleep Research&rft.volume=&rft.spage=&rft.date=2020
dc.identifier.jtitleJournal of Sleep Research
dc.identifier.doihttps://doi.org/10.1111/jsr.13036
dc.identifier.urnURN:NBN:no-85679
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn0962-1105
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/82872/4/jsr.13036.pdf
dc.type.versionPublishedVersion
cristin.articleide13036


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