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.
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References
Prashanth, R., Roy, S.D., Mandal, P.K., Ghosh, S.: High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning. Int. J. Med. Inform. 90, 13–21 (2016)
Doppler, K., et al.: Dermal phosphor-alpha-synuclein deposits confirm REM sleep behaviour disorder as prodromal Parkinson’s disease. Acta Neuropathol. 133(4), 535–545 (2017)
Postuma, R., et al.: Quantifying the risk of neurodegenerative disease in idiopathic REM sleep behavior disorder. Neurology 72(15), 1296–1300 (2009)
Iranzo, A., et al.: Rapid-eye-movement sleep behaviour disorder as an early marker for a neurodegenerative disorder: a descriptive study. Lancet Neurol. 5(7), 572–577 (2006)
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)
Malhotra, A., et al.: Performance of an automated polysomnography scoring system versus computer-assisted manual scoring. Sleep 36(4), 573–582 (2013)
Collop, N.A.: Coring variability between polysomnography technologists in different sleep laboratories. Sleep Med. 3(1), 43–50 (2002)
Ferri, R., et al.: A new quantitative automatic method for the measurement of non-rapid eye movement sleep electroencephalographic amplitude variability. J. Sleep Res. 21, 212–220 (2012)
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)
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)
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)
Moser, D., et al.: Sleep classification according to AASM and Rechtschaffen & Kales: effects on sleep scoring parameters. Sleep 32(2), 139–49 (2009)
Boeve, B.F., et al.: Pathophysiology of REM sleep behaviour disorder and relevance to neurodegenerative disease. Brain 130(11), 2770–2788 (2007)
Boostani, R., Karimzadeh, F., Nami, M.: A comparative review on sleep stage classification methods in patients and healthy individuals. Comput. Methods Programs Biomed. 140, 77–91 (2017)
Khalighi, S., Sousa, T., Pires, G., Nunes, U.: Automatic sleep staging: a computer assisted approach for optimal combination of features and polysomnographic channels. Expert Syst. Appl. 40(17), 7046–7059 (2013)
Zhu, G., Li, Y., Wen, P.: Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal. IEEE J. Biomed. Health Inform. 18(6), 1813–1821 (2014)
Mohamad, I.B., Usman, D.: Standardization and its effects on K-means clustering algorithm. Res. J. Appl. Sci. Eng. Technol. 6(17), 3299–3303 (2013)
Ross, B.C.: Mutual information between discrete and continuous data sets. PLoS ONE 9(2), e87357 (2014)
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)
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)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 1, no. 10. Springer, New York (2001)
Cristianini, N., Shawe-Taylor, J., et al.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)
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)
Breiman, L.: Random forests - random features technical report 576, Statistical Department, UC Berkeley, USA (1999)
Kumar, M., Sheshadri, H.: On the classification of imbalanced datasets. Int. J. Comput. Appl. 44(8), 1–7 (2012)
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)
Zhang, J., Yao, R., Ge, W., Gao, J.: Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. Comput. Methods Programs Biomed. 183, 105089 (2020)
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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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Bisgin, P., Houta, S., Burmann, A., Lenfers, T. (2020). REM Sleep Stage Detection of Parkinson’s Disease Patients with RBD. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_3
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DOI: https://doi.org/10.1007/978-3-030-53337-3_3
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