Sleep stage classification for managing nocturnal enuresis through effective configuration

Various studies have examined the quality of one'sasleep and further investigated several sleep disorders. In thoseainvestigations, accurately classifying one's sleep into the standardized sleep stages is important. The conventional classification heavily depends on the manual examination of each expert on one's physiological signals during the sleep. Therefore, various automatic classification models have been proposed using the machine learning. Although they properly classify the sleep stages on average, there have been few investigations to specifically improve the classification accuracy of certain stages. Accurate determination of several stages considerably correlating with a disorder gives us a more effective hint to conquer the disorder. Accordingly, we propose a configured classification model focusing on the interesting sleep stages related to a challenging sleep disorder, the nocturnal enuresis. We consider the deterministic physiological signals of the interesting stages when training the classifiers. Further, the proposed system utilizes recurrent neural network to effectively learn the sequential feature of the physiological data. Our proposed system achieves the classification accuracy by 83.6% over the data. In particular, technique presents up to 15.5% higher accuracy to differentiate interesting stages than the support vector machine approach for the nocturnal enuresis.