On all epochs, our model achieves an accuracy of 82.7%, which is only slightly lower than the inter-rater disagreement. A thorough evaluation of our method is performed on a multi-labeled dataset in which both the inter-rater variability as well as the prediction uncertainties are taken into account, leading to a new standard for the evaluation of automated sleep stage scoring algorithms. However, given the same data, two expert annotators will construct (slightly) different hypnograms due to differing interpretations of the data or individual mistakes. A common way to evaluate automated approaches to constructing hypnograms is to compare the one produced by the algorithm to an expert's hypnogram. The model mostly relies on features based on the rules from the American Academy of Sleep Medicine, which allows medical experts to gain insights into the model. 5Department of Pulmonary Medicine, Antwerp University Hospital, Antwerp, BelgiumĪ new method for automated sleep stage scoring of polysomnographies is proposed that uses a random forest approach to model feature interactions and temporal effects.4Multidisciplinary Sleep Disorders Centre, Antwerp University Hospital, Antwerp, Belgium.3Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.2Department of Computer Science, Leuven AI, KU Leuven, Leuven, Belgium.1Onafhankelijke Software Groep (OSG bv), Micromed Group, Kontich, Belgium.Dries Van der Plas 1,2,3 * Johan Verbraecken 3,4,5 Marc Willemen 4 Wannes Meert 2 Jesse Davis 2
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