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Metric Learning for Automatic Sleep Stage Classification

Phan, Huy, Do, Quan, Do, The-Luan, Vu, Duc-Lung (2013) Metric Learning for Automatic Sleep Stage Classification. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013). . pp. 5025-5028. IEEE E-ISBN 978-1-4577-0216-7. (doi:10.1109/EMBC.2013.6610677)

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https://doi.org/10.1109/EMBC.2013.6610677

Abstract

We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/EMBC.2013.6610677
Divisions: Faculties > Sciences > School of Computing
Depositing User: Huy Phan
Date Deposited: 25 Feb 2019 17:28 UTC
Last Modified: 03 Jun 2019 09:28 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72696 (The current URI for this page, for reference purposes)
Phan, Huy: https://orcid.org/0000-0003-4096-785X
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