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Bayesian fusion of algorithms for the robust estimation of respiratory rate from the photoplethysmogram

Abstract:

Respiratory rate (RR) is a key vital sign that is monitored to assess the health of patients. With the increase of the availability of wearable devices, it is important that RR is extracted in a robust and noninvasive manner from the photoplethysmogram (PPG) acquired from pulse oximeters and similar devices. However, existing methods of noninvasive RR estimation suffer from a lack of robustness, resulting in the fact that they are not used in clinical practice.

We propose a Bayesian...

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

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Publisher copy:
10.1109/EMBC.2015.7319793

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Clifford, GD More by this author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Kellogg College More from this funder
Royal Academy of Engineering More from this funder
Balliol College More from this funder
Publisher:
IEEE Publisher's website
Volume:
2015-November
Pages:
6138-6141
Publication date:
2015-11-05
DOI:
ISSN:
1557-170X
URN:
uuid:c32ba168-e951-4cdd-8a2d-934f26fd4168
Source identifiers:
592450
Local pid:
pubs:592450
ISBN:
9781424492718

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