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 language | English |
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Title of host publication | 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Number of pages | 4 |
Publisher | IEEE |
Publication date | 01.07.2013 |
Pages | 5025-5028 |
Article number | 6610677 |
ISBN (Electronic) | 978-1-4577-0216-7 |
DOIs | |
Publication status | Published - 01.07.2013 |
Event | 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Osaka, Japan Duration: 03.07.2013 → 07.07.2013 |