REM Sleep Stage Detection of Parkinson’s Disease Patients with RBD
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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.
KeywordsParkinson’s disease Neurodegenerative disease PCompanion Sleep stage PSG RBD Classification
The authors acknowledge the public funding by the Federal Ministry of Education and Research of Germany in the framework of PCompanion (project number V5IKM011).
- 5.Younes, M., Thompson, W., Leslie, C., Equan, T., Giannouli, E.: Utility of technologist editing of polysomnography scoring performed by a validated automatic system. Ann. Am. Thorac. Soc. 12(8), 1206–1218 (2015)Google Scholar
- 9.Chiu, C.C., Hai, B.H., Yeh, S.J.: Recognition of sleep stage based on a combined neural network and fuzzy system using wavelet transform features. Biomed. Eng.: Appl. Basis Commun. 26(2), 1450021–1450029 (2014)Google Scholar
- 10.Rechtschaffen, A., Kales, A. (eds.): A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects, no. 204. National Institutes of Health Publications, U.S. Government Printing Office (1968)Google Scholar
- 11.Iber, C., Ancoli-Israel, S., Chesson, A., Quan, S.F.: The AASM Manual for the Scoring of Sleep and Associated Events, 1st edn. American Academy of Sleep Medicine, Westchester (2007)Google Scholar
- 19.Yun, C., Shin, D., Jo, H., Yang, J., Kim, S.: An experimental study on feature subset selection methods. In: 7th IEEE International Conference on Computer and Information Technology (CIT 2007), pp. 77–82. IEEE (2007)Google Scholar
- 20.Agrawal, R., Ram, B.: A modified k-nearest neighbor algorithm to handle uncertain data. In: 2015 5th International Conference on IT Convergence and Security (ICITCS), pp. 1–4. IEEE (2015)Google Scholar
- 21.Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 1, no. 10. Springer, New York (2001)Google Scholar
- 23.Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2017)Google Scholar
- 24.Breiman, L.: Random forests - random features technical report 576, Statistical Department, UC Berkeley, USA (1999)Google Scholar
- 25.Kumar, M., Sheshadri, H.: On the classification of imbalanced datasets. Int. J. Comput. Appl. 44(8), 1–7 (2012)Google Scholar
- 26.Kirchner, J., Faghih-Naini, S., Bisgin, P., Fischer, G.: Sensor selection for classification of physical activity in long-term wearable devices. In: IEEE Sensors, pp. 1–4 (2018)Google Scholar