@inproceedings{290cd0d1f4aa45f3892d2a9212365494,
title = "Sleep stages classification from electroencephalographic signals based on unsupervised feature space clustering",
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.",
keywords = "Clustering, Electroencephalography, Sleep stages",
author = "Iosif Mporas and Anastasia Efstathiou and Vasileios Megalooikonomou",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-23344-4_8",
language = "English",
isbn = "9783319233437",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature Link",
pages = "77--85",
editor = "{Guo Y.}, Yike and {Hill S.}, Sean and Karl Friston and Hanchuan Peng and {Faisal A.}, Aldo",
booktitle = "Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings",
address = "Netherlands",
note = "8th International Conference on Brain Informatics and Health, BIH 2015 ; Conference date: 30-08-2015 Through 02-09-2015",
}