TeaSpam: A Novel Method of TEmporal And SPAtial Movement Encoding during Sleep

Kohn, Bernhard; Ruzicka, Laurenz; Högl, Birgit; Ibrahim, Abubaker; Garn, Heinrich; Heidbreder, Anna; Bergmann, Melanie; Brandauer, Elisabeth; Holzknecht, Evi; Stefani, Ambra; Cesari, Matteo

doi:10.1109/embc48229.2022.9871521

Cited by 1 publication

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“…More specifically, movements in the lower body (i.e., the region including the knees and the area below them, as previously proposed [ 12 ]), head, hands (including one or both hands) and upper body (region above the hip) were identified. Whilst upper and lower body ROIs were manually identified, head and hands ROIs were dynamically identified with a validated deep neural network as described elsewhere [ 22 ]. Such neural network was trained to identify head and hands when not covered by the blankets.…”

Section: Methodsmentioning

confidence: 99%

Improved automatic identification of isolated rapid eye movement sleep behavior disorder with a 3D time‐of‐flight camera

Cesari

Ruzicka

Högl

et al. 2023

Euro J of Neurology

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Background and purpose Automatic 3D video analysis of the lower body during rapid eye movement (REM) sleep has been recently proposed as a novel tool for identifying people with isolated REM sleep behavior disorder (iRBD), but, so far, it has not been validated on unseen subjects. This study aims at validating this technology in a large cohort and at improving its performances by also including an analysis of movements in the head, hands and upper body. Methods Fifty‐three people with iRBD and 128 people without RBD (of whom 89 had sleep disorders considered RBD differential diagnoses) were included in the study. An automatic algorithm identified movements from 3D videos during REM sleep in four regions of interest (ROIs): head, hands, upper body and lower body. The movements were divided into categories according to duration: short (0.1–2 s), medium (2–15 s) and long (15–300 s). For each ROI and duration range, features were obtained from the identified movements. Logistic regression models using as predictors the features from one single ROI or a combination of ROIs were trained and tested in a 10‐runs 10‐fold cross‐validation scheme on the task of differentiating people with iRBD from people without RBD. Results The best differentiation was achieved using short movements in all four ROIs (test accuracy 0.866 ± 0.007, test F1 score = 0.783 ± 0.010). Single group analyses showed that people with iRBD were distinguished successfully from subjects with RBD differential diagnoses. Conclusions Automatic 3D video analysis might be implemented in clinical routine as a supportive screening tool for identifying people with RBD.

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Section: Methodsmentioning

confidence: 99%