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Bayesian fusion of physiological measurements using a signal quality extension

Abstract:

Objective: The fusion of multiple noisy labels for biomedical data (such as ECG annotations, which may be obtained from human experts or from automated systems) into a single robust annotation has many applications in physiologic monitoring. Directly modelling the difficulty of the task has the potential to improve the fusion of such labels. This paper proposes a means for the incorporation of task difficulty, as quantified by ‘signal quality’, into the fusion process. Approach: We propose a ...

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

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Publisher copy:
10.1088/1361-6579/aac856

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
ORCID:
0000-0002-1552-5630
Johnson, AEW More by this author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Clifford, GD More by this author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
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Funding agency for:
Clifton, DA
Publisher:
IOP Publishing Publisher's website
Journal:
Physiological Measurement Journal website
Volume:
39
Article number:
065008
Publication date:
2018-06-27
Acceptance date:
2018-05-29
DOI:
EISSN:
1361-6579
ISSN:
0967-3334
Pubs id:
pubs:854524
URN:
uri:a074a0ef-68f3-454f-8786-4e250be23306
UUID:
uuid:a074a0ef-68f3-454f-8786-4e250be23306
Local pid:
pubs:854524

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