Contact‐free radar recordings of body movement can reflect ultradian dynamics of sleep

Autor: Hanne Siri Amdahl Heglum, Henning Johannes Drews, Håvard Kallestad, Daniel Vethe, Knut Langsrud, Trond Sand, Morten Engstrøm
Rok vydání: 2022
Předmět:
Zdroj: Heglum, H S A, Drews, H J, Kallestad, H, Vethe, D, Langsrud, K, Sand, T & Engstrom, M 2022, ' Contact-free radar recordings of body movement can reflect ultradian dynamics of sleep ', Journal of Sleep Research, vol. 31, no. 6, 13687 . https://doi.org/10.1111/jsr.13687
Journal of Sleep Research
ISSN: 1365-2869
0962-1105
DOI: 10.1111/jsr.13687
Popis: This 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 (r(radars) >0.80, r(actigraph) >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.
Databáze: OpenAIRE
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