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REM Sleep Stage Detection of Parkinson’s Disease Patients with RBD

: Bisgin, Pinar; Houta, Salima; Burmann, Anja; Lenfers, Tim


Abramovicz, Witold:
Business Information Systems. 23rd International Conference, BIS 2020. Proceedings : Colorado Springs, CO, USA, June 8-10, 2020
Cham: Springer Nature, 2020 (Lecture Notes in Business Information Processing 389)
ISBN: 978-3-030-53336-6 (Print)
ISBN: 978-3-030-53337-3 (Online)
International Conference on Business Information Systems (BIS) <23, 2020, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
V5IKM011; PCompanion
Conference Paper
Fraunhofer ISST ()
Parkinsons disease Neurodegenerative disease; PCompanion; sleep stage; PSG; RBD; classification

REM sleep behavior disorder (RBD) is commonly associated with Parkinson’s disease. In order to find adequate therapy for affected persons and to seek suitable early Parkinson Patterns, the investigation of this phenomenon is highly relevant. The analysis of sleep is currently done by manual analysis of polysomnography (PSG), which leads to divergent scoring results by different experts. Automated sleep stage detection can help deliver accurate, reproducible scoring results. In this paper, we evaluate different machine learning models from the PSG signals for automatic sleep stage detection. The highest accuracy of 87.57% was achieved by using the Random Forest algorithm.