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

Conference paper
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Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 389)

Abstract

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.

Keywords

Parkinson’s disease Neurodegenerative disease PCompanion Sleep stage PSG RBD Classification 

Notes

Acknowledgement

The authors acknowledge the public funding by the Federal Ministry of Education and Research of Germany in the framework of PCompanion (project number V5IKM011).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Fraunhofer Institute for Software and Systems Engineering, HealthCareDortmundGermany
  2. 2.Department of Medical InformaticsHeilbronn UniversityHeilbronnGermany
  3. 3.Ruprecht-Karls University HeidelbergHeidelbergGermany

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