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Interactive Sleep Stage Labelling Tool For Diagnosing Sleep Disorder Using Deep Learning

Title
Interactive Sleep Stage Labelling Tool For Diagnosing Sleep Disorder Using Deep Learning
Author
김영훈
Issue Date
2018-07
Publisher
IEEE
Citation
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Page. 183-186
Abstract
Traditional manual scoring of the entire sleep for diagnosis of sleep disorders is highly time-consuming and dependent to experts experience. Thus, automatic methods based on electrooculography (EOG) analysis have been increasingly attracted attentions to lower the cost of scoring. Such computeraided diagnosis of sleep disorders are usually based on the 6 scores, wake (W), sleep status (S1-S4) and REM by labelling every 30-second long EOG records. This paper presents an automatic scoring method of sleep stages by using the recent advancements in deep learning. We also suggest an interactive scoring scheme to enable the doctors of practitioners to give feedback by correcting errors and improve the accuracy of scoring as well as diagnosis of sleep disorders.
URI
https://ieeexplore.ieee.org/abstract/document/8512219http://repository.hanyang.ac.kr/handle/20.500.11754/121398
ISBN
978-1-5386-3646-6
ISSN
1558-4615
DOI
10.1109/EMBC.2018.8512219
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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