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Conf Proc IEEE Eng Med Biol Soc. 2013;2013:5025-8. doi: 10.1109/EMBC.2013.6610677.

Metric learning for automatic sleep stage classification.

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.

PMID:
24110864
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