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AuthorAl-Meer M.H.
AuthorAl Mamun M.D.A.
Available date2020-03-03T06:19:33Z
Publication Date2018
Publication Name2018 13th International Conference on Digital Information Management, ICDIM 2018
URIhttp://dx.doi.org/10.1109/ICDIM.2018.8846973
URIhttp://hdl.handle.net/10576/13169
AbstractThis paper presents a deep feed-forward neural network classifier to automatically classify the stages of sleep using raw data taken from a single electropalatogram channel (Fpz-Cz). No features are extracted at all from the data, and the network can classify the five sleep stages: waking, Nl, N2, N3, N4, and rapid eye movement. The network has three layers, takes as an input a l-s epochs to be classified, and requires no signal pre-processing nor feature extraction. We trained and evaluated our system using DeepLearning4J, the free Java framework for test data taken from PhysioNet's Polysomnography Sleep database. An accuracy of 0.99 within a constrained environment has been reached.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
dc.source Scopus
SubjectDeep Learning
SubjectMachine Learning
SubjectPSG
SubjectSleep Stages
TitleDeep learning in classifying sleep stages
TypeConference Paper
Pagination17-Dec


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