TY - GEN
T1 - Sleep stage classification for managing nocturnal enuresis through effective configuration
AU - Lee, Sangyeop
AU - Moon, Junhyung
AU - Lee, Taeho
AU - Kye, Saewon
AU - Lee, Kyoungwoo
AU - Lee, Yong Seung
AU - Shin, Seung Chul
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85044234747&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044234747&partnerID=8YFLogxK
U2 - 10.1109/SMC.2017.8123056
DO - 10.1109/SMC.2017.8123056
M3 - Conference contribution
AN - SCOPUS:85044234747
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 2832
EP - 2837
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Y2 - 5 October 2017 through 8 October 2017
ER -