@article{KaulenSchwabedalSchneideretal.2022,
  author    = {Kaulen, Lars and Schwabedal, Justus T. C. and Schneider, Jules and Ritter, Philipp and Bialonski, Stephan},
  title     = {Advanced sleep spindle identification with neural networks},
  series = {Scientific Reports},
  volume    = {12},
  journal   = {Scientific Reports},
  number    = {Article number: 7686},
  publisher = {Springer Nature},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-022-11210-y},
  pages     = {1 -- 10},
  year      = {2022},
  abstract  = {Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.},
  language  = {en}
}
@article{RoethenbacherCesariDoppleretal.2022,
  author    = {R{\"o}thenbacher, Annika and Cesari, Matteo and Doppler, Christopher E.J. and Okkels, Niels and Willemsen, Nele and Sembowski, Nora and Seger, Aline and Lindner, Marie and Brune, Corinna and Stefani, Ambra and H{\"o}gl, Birgit and Bialonski, Stephan and Borghammer, Per and Fink, Gereon R. and Schober, Martin and Sommerauer, Michael},
  title     = {RBDtector: an open-source software to detect REM sleep without atonia according to visual scoring criteria},
  series = {Scientific Reports},
  volume    = {12},
  journal   = {Scientific Reports},
  number    = {Article number: 20886},
  publisher = {Springer Nature},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-022-25163-9},
  pages     = {1 -- 14},
  year      = {2022},
  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.},
  language  = {en}
}