Metric learning for automatic sleep stage classification

H. Phan, Q. Do, T. Do, D. Vu

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.
Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Number of pages4
PublisherIEEE
Publication date01.07.2013
Pages5025-5028
Article number6610677
ISBN (Electronic)978-1-4577-0216-7
DOIs
Publication statusPublished - 01.07.2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Osaka, Japan
Duration: 03.07.201307.07.2013

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