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SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging

Abstract:

Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http://github.com/pquochu...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1109/tnsre.2019.2896659

Authors


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Role:
Author
ORCID:
0000-0003-4096-785X
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-6868-4718
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
ORCID:
0000-0002-5696-3127
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
Kellogg College
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Transactions on Neural Systems and Rehabilitation Engineering Journal website
Volume:
27
Issue:
3
Pages:
400-410
Publication date:
2019-01-31
Acceptance date:
2019-01-27
DOI:
EISSN:
1558-0210
ISSN:
1534-4320
Pubs id:
pubs:967940
URN:
uri:6eaf8810-bad1-4127-9f17-199441aece13
UUID:
uuid:6eaf8810-bad1-4127-9f17-199441aece13
Local pid:
pubs:967940

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