Academic Journal

RBDtector: an open-source software to detect REM sleep without atonia according to visual scoring criteria

التفاصيل البيبلوغرافية
العنوان: RBDtector: an open-source software to detect REM sleep without atonia according to visual scoring criteria
المؤلفون: Annika Röthenbacher, Matteo Cesari, Christopher E. J. Doppler, Niels Okkels, Nele Willemsen, Nora Sembowski, Aline Seger, Marie Lindner, Corinna Brune, Ambra Stefani, Birgit Högl, Stephan Bialonski, Per Borghammer, Gereon R. Fink, Martin Schober, Michael Sommerauer
المصدر: Scientific Reports, Vol 12, Iss 1, Pp 1-14 (2022)
بيانات النشر: Nature Portfolio, 2022.
سنة النشر: 2022
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: Abstract REM sleep without atonia (RSWA) is a key feature for the diagnosis of rapid eye movement (REM) sleep behaviour disorder (RBD). We introduce RBDtector, a novel open-source software to score RSWA according to established SINBAR visual scoring criteria. We assessed muscle activity of the mentalis, flexor digitorum superficialis (FDS), and anterior tibialis (AT) muscles. RSWA was scored manually as tonic, phasic, and any activity by human scorers as well as using RBDtector in 20 subjects. Subsequently, 174 subjects (72 without RBD and 102 with RBD) were analysed with RBDtector to show the algorithm’s applicability. We additionally compared RBDtector estimates to a previously published dataset. RBDtector showed robust conformity with human scorings. The highest congruency was achieved for phasic and any activity of the FDS. Combining mentalis any and FDS any, RBDtector identified RBD subjects with 100% specificity and 96% sensitivity applying a cut-off of 20.6%. Comparable performance was obtained without manual artefact removal. RBD subjects also showed muscle bouts of higher amplitude and longer duration. RBDtector provides estimates of tonic, phasic, and any activity comparable to human scorings. RBDtector, which is freely available, can help identify RBD subjects and provides reliable RSWA metrics.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-022-25163-9
URL الوصول: https://doaj.org/article/22ea50bbf3d8408b8ec112e28c27d7a7
رقم الانضمام: edsdoj.22ea50bbf3d8408b8ec112e28c27d7a7
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-022-25163-9