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dc.contributor.authorHeglum, Hanne Siri Amdahl
dc.contributor.authorDrews, Henning Johannes
dc.contributor.authorKallestad, Håvard
dc.contributor.authorVethe, Daniel
dc.contributor.authorLangsrud, Knut
dc.contributor.authorSand, Trond
dc.contributor.authorEngstrøm, Morten
dc.date.accessioned2023-04-12T09:23:18Z
dc.date.available2023-04-12T09:23:18Z
dc.date.created2022-09-27T17:47:48Z
dc.date.issued2022
dc.identifier.citationJournal of Sleep Research. 2022, 1-14.en_US
dc.identifier.issn0962-1105
dc.identifier.urihttps://hdl.handle.net/11250/3062572
dc.description.abstractThis work aimed to evaluate if a contact-free radar sensor can be used to observe ultradian patterns in sleep physiology, by way of a data processing tool known as Locomotor Inactivity During Sleep (LIDS). LIDS was designed as a simple transformation of actigraphy recordings of wrist movement, meant to emphasise and enhance the contrast between movement and non-movement and to reveal patterns of low residual activity during sleep that correlate with ultradian REM/NREM cycles. We adapted the LIDS transformation for a radar that detects body movements without direct contact with the subject and applied it to a dataset of simultaneous recordings with polysomnography, actigraphy, and radar from healthy young adults (n = 12, four nights of polysomnography per participant). Radar and actigraphy-derived LIDS signals were highly correlated with each other (r > 0.84), and the LIDS signals were highly correlated with reduced-resolution polysomnographic hypnograms (rradars >0.80, ractigraph >0.76). Single-harmonic cosine models were fitted to LIDS signals and hypnograms; significant differences were not found between their amplitude, period, and phase parameters. Mixed model analysis revealed similar slopes of decline per cycle for radar-LIDS, actigraphy-LIDS, and hypnograms. Our results indicate that the LIDS technique can be adapted to work with contact-free radar measurements of body movement; it may also be generalisable to data from other body movement sensors. This novel metric could aid in improving sleep monitoring in clinical and real-life settings, by providing a simple and transparent way to study ultradian dynamics of sleep using nothing more than easily obtainable movement data.en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleContact-free radar recordings of body movement can reflect ultradian dynamics of sleepen_US
dc.title.alternativeContact-free radar recordings of body movement can reflect ultradian dynamics of sleepen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber1-14en_US
dc.source.journalJournal of Sleep Researchen_US
dc.identifier.doi10.1111/jsr.13687
dc.identifier.cristin2056077
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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