@article{SchneiderSchwabedalBialonski2022,
  author    = {Schneider, Jules and Schwabedal, Justus T. C. and Bialonski, Stephan},
  title     = {Schlafspindeln - Funktion, Detektion und Nutzung als Biomarker f{\"u}r die psychiatrische Diagnostik},
  series = {Der Nervenarzt},
  journal   = {Der Nervenarzt},
  publisher = {Springer},
  address   = {Berlin, Heidelberg},
  issn      = {1433-0407},
  doi       = {10.1007/s00115-022-01340-z},
  pages     = {1 -- 8},
  year      = {2022},
  abstract  = {Hintergrund: Die Schlafspindel ist ein Graphoelement des Elektroenzephalogramms (EEG), das im Leicht- und Tiefschlaf beobachtet werden kann. Ver{\"a}nderungen der Spindelaktivit{\"a}t wurden f{\"u}r verschiedene psychiatrische Erkrankungen beschrieben. Schlafspindeln zeigen aufgrund ihrer relativ konstanten Eigenschaften Potenzial als Biomarker in der psychiatrischen Diagnostik. Methode: Dieser Beitrag liefert einen {\"U}berblick {\"u}ber den Stand der Wissenschaft zu Eigenschaften und Funktionen der Schlafspindeln sowie {\"u}ber beschriebene Ver{\"a}nderungen der Spindelaktivit{\"a}t bei psychiatrischen Erkrankungen. Verschiedene methodische Ans{\"a}tze und Ausblicke zur Spindeldetektion werden hinsichtlich deren Anwendungspotenzial in der psychiatrischen Diagnostik erl{\"a}utert. Ergebnisse und Schlussfolgerung: W{\"a}hrend Ver{\"a}nderungen der Spindelaktivit{\"a}t bei psychiatrischen Erkrankungen beschrieben wurden, ist deren exaktes Potenzial f{\"u}r die psychiatrische Diagnostik noch nicht ausreichend erforscht. Diesbez{\"u}glicher Erkenntnisgewinn wird in der Forschung gegenw{\"a}rtig durch ressourcenintensive und fehleranf{\"a}llige Methoden zur manuellen oder automatisierten Spindeldetektion ausgebremst. Neuere Detektionsans{\"a}tze, die auf Deep-Learning-Verfahren basieren, k{\"o}nnten die Schwierigkeiten bisheriger Detektionsmethoden {\"u}berwinden und damit neue M{\"o}glichkeiten f{\"u}r die praktisch},
  language  = {de}
}
@inproceedings{BlaneckBornheimGriegeretal.2022,
  author    = {Blaneck, Patrick Gustav and Bornheim, Tobias and Grieger, Niklas and Bialonski, Stephan},
  title     = {Automatic readability assessment of german sentences with transformer ensembles},
  series = {Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text},
  booktitle = {Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text},
  publisher = {Association for Computational Linguistics},
  address   = {Potsdam},
  doi       = {10.48550/arXiv.2209.04299},
  pages     = {57 -- 62},
  year      = {2022},
  abstract  = {Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0:435.},
  language  = {en}
}
@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}
}