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.
|Title of host publication||2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)|
|Number of pages||4|
|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