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Joint classification and prediction CNN framework for automatic sleep stage classification

Abstract:

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This work proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs' labels (prediction) in the contextual output. W...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's Version

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Publisher copy:
10.1109/TBME.2018.2872652

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
NIHR Oxford Biomedical Research Centre More from this funder
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Transactions on Biomedical Engineering Journal website
Volume:
66
Issue:
5
Pages:
1285-1296
Publication date:
2018-10-22
Acceptance date:
2018-09-22
DOI:
EISSN:
1558-2531
ISSN:
0018-9294
Pubs id:
pubs:930436
URN:
uri:e871983d-04f6-4bee-ab08-75f354f2862b
UUID:
uuid:e871983d-04f6-4bee-ab08-75f354f2862b
Local pid:
pubs:930436

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