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Actimeter sleep6/22/2023 ![]() Sleep actigraphs are generally watch-shaped and worn on the wrist of the non-dominant arm for adults and usually on the ankle for children. The data can be later read to a computer and analysed offline in some brands of sensors the data are transmitted and analysed in real time. The movements the actigraph unit undergoes are continually recorded and some units also measure light exposure. The unit is usually in a wristwatch-like package worn on the wrist. A small actigraph unit, also called an actimetry sensor, is worn for a week or more to measure gross motor activity. JSTOR ( January 2010) ( Learn how and when to remove this template message)Īctigraphy is a non-invasive method of monitoring human rest/activity cycles.Unsourced material may be challenged and removed. ![]() Please help improve this article by adding citations to reliable sources. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.This article needs additional citations for verification. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society. With its good performance across daytime and night-time, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep-wake patterns in field studies. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically differently from polysomnography in other sleep parameters. Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm reached a median sensitivity of 92% (85%-100%) but low specificity of 33% (10%-98%), owing to the low frequency of wake episodes in the night-time polysomnographic recordings. Mean onset and offset times were highly correlated (r =. Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range = 75%-86%) and specificity of 91% (87%-92%). Epoch-by-epoch analyses were conducted and comparisons of sleep measures carried out via Bland-Altman plots and correlations. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing one night of polysomnography. Sleep-log validation was performed on two field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. The Munich Actimetry Sleep Detection Algorithm uses a moving 24-h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm. Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings Please use this identifier to cite or link to this item:
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