Abstract
In this article we present a methodology for the automatic classification of sleep stages. The methodology relies on short-time analysis with time and frequency domain features followed by unsupervised feature subspace clustering. For each cluster of the feature space a different classification setup is adopted thus fine-tuning the classification algorithm to the specifics of the corresponding feature subspace area. The experimental results showed that the proposed methodology achieved a sleep stage classification accuracy equal to 92.53%, which corresponds to an improvement of approximately 3% compared to the best performing single classifier without applying clustering of the feature space.
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Mporas, I., Efstathiou, A., Megalooikonomou, V. (2015). Sleep Stages Classification from Electroencephalographic Signals Based on Unsupervised Feature Space Clustering. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_8
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