Contact‐free radar recordings of body movement can reflect ultradian dynamics of sleep. (6th July 2022)

• Record Type: Journal Article
• Authors: Heglum, Hanne Siri Amdahl; Drews, Henning Johannes; Kallestad, Håvard; Vethe, Daniel; Langsrud, Knut; Sand, Trond; Engstrøm, Morten
• Is Part Of: Journal of sleep research. Volume 31:Number 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-06
• Subjects: actigraphy -- LIDS -- REM/NREM cycles -- sleep monitoring -- UWB radar; Sleep -- Periodicals; Sleep disorders -- Periodicals; 612.821
• DOI: 10.1111/jsr.13687
• Languages: English
• ISSNs: 0962-1105

Abstract

Summary: 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.