Dataset Information

Distinguishing sleep from wake with a radar sensor: a contact-free real-time sleep monitor.

ABSTRACT: This work aimed to evaluate whether a radar sensor can distinguish sleep from wakefulness in real time. The sensor detects body movements without direct physical contact with the subject and can be embedded in the roof of a hospital room for completely unobtrusive monitoring. We conducted simultaneous recordings with polysomnography, actigraphy, and radar on two groups: healthy young adults (n = 12, four nights per participant) and patients referred to a sleep examination (n = 28, one night per participant). We developed models for sleep/wake classification based on principles commonly used by actigraphy, including real-time models, and tested them on both datasets. We estimated a set of commonly reported sleep parameters from these data, including total-sleep-time, sleep-onset-latency, sleep-efficiency, and wake-after-sleep-onset, and evaluated the inter-method reliability of these estimates. Classification results were on-par with, or exceeding, those often seen for actigraphy. For real-time models in healthy young adults, accuracies were above 92%, sensitivities above 95%, specificities above 83%, and all Cohen's kappa values were above 0.81 compared to polysomnography. For patients referred to a sleep examination, accuracies were above 81%, sensitivities about 89%, specificities above 53%, and Cohen's kappa values above 0.44. Sleep variable estimates showed no significant intermethod bias, but the limits of agreement were quite wide for the group of patients referred to a sleep examination. Our results indicate that the radar has the potential to offer the benefits of contact-free real-time monitoring of sleep, both for in-patients and for ambulatory home monitoring.

SUBMITTER: Heglum HSA

PROVIDER: S-EPMC8361351 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Distinguishing sleep from wake with a radar sensor: a contact-free real-time sleep monitor.

Heglum Hanne Siri Amdahl HSA Kallestad Håvard H Vethe Daniel D Langsrud Knut K Sand Trond T Engstrøm Morten M

Sleep 20210801 8

This work aimed to evaluate whether a radar sensor can distinguish sleep from wakefulness in real time. The sensor detects body movements without direct physical contact with the subject and can be embedded in the roof of a hospital room for completely unobtrusive monitoring. We conducted simultaneous recordings with polysomnography, actigraphy, and radar on two groups: healthy young adults (n = 12, four nights per participant) and patients referred to a sleep examination (n = 28, one night per ...[more]

PMID: 33705555

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Project description:BackgroundDifferentiating nonwear time from sleep and wake times is essential for the estimation of sleep duration based on actigraphy data. To efficiently analyze large-scale data sets, an automatic method of identifying these three different states is required. Therefore, we developed a classification algorithm to determine nonwear, sleep and wake periods from accelerometer data. Our work aimed to (I) develop a new pattern recognition algorithm for identifying nonwear periods from actigraphy data based on the influence of respiration rate on the power spectrum of the acceleration signal and implement it in an automatic classification algorithm for nonwear/sleep/wake states; (II) address motion artifacts that occur during nonwear periods and are known to cause misclassification of these periods; (III) adjust the algorithm depending on the sensor position (wrist, chest); and (IV) validate the algorithm on both healthy individuals and patients with sleep disorders.MethodsThe study involved 98 participants who wore wrist and chest acceleration sensors for one day of measurements. They spent one night in the sleep laboratory and continued to wear the sensors outside of the laboratory for the remainder of the day. The results of the classification algorithm were compared to those of the reference source: polysomnography for wake/sleep and manual annotations for nonwear/wear classification.ResultsThe median kappa values for the two locations were 0.83 (wrist) and 0.84 (chest). The level of agreement did not vary significantly by sleep health (good sleepers vs. subjects with sleep disorders) (p = 0.348, p = 0.118) or by sex (p = 0.442, p = 0.456). The intraclass correlation coefficients of nonwear total time between the reference and the algorithm were 0.92 and 0.97 with the outliers and 0.95 and 0.98 after the outliers were removed for the wrist and chest, respectively. There was no evidence of an association between the mean difference (and 95% limits of agreement) and the mean of the two methods for either sensor position (wrist p = 0.110, chest p = 0.164), and the mean differences (algorithm minus reference) were 5.11 [95% LoA -15.4-25.7] and 1.32 [95% LoA -9.59-12.24] min/day, respectively, after the outliers were removed.DiscussionWe studied the influence of the respiration wave on the power spectrum of the acceleration signal for the differentiation of nonwear periods from sleep and wake periods. The algorithm combined both spectral analysis of the acceleration signal and rescoring. Based on the Bland-Altman analysis, the chest-worn accelerometer showed better results than the wrist-worn accelerometer.

Dataset Information

Distinguishing sleep from wake with a radar sensor: a contact-free real-time sleep monitor.

Publications

Distinguishing sleep from wake with a radar sensor: a contact-free real-time sleep monitor.

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