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PlethAugment: GAN-based PPG augmentation for medical diagnosis in low-resource settings

Abstract:

The paucity of physiological time-series data collected from low-resource clinical settings limits the capabilities of modern machine learning algorithms in achieving high performance. Such performance is further hindered by class imbalance; datasets where a diagnosis is much more common than others. To overcome these two issues at low-cost while preserving privacy, data augmentation methods can be employed. In the time domain, the traditional method of time-warping could alter the underlying...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/JBHI.2020.2979608

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Cross College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Tropical Medicine
Role:
Author
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Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Journal of Biomedical and Health Informatics Journal website
Volume:
24
Issue:
11
Article number:
3226-3235
Publication date:
2020-04-27
Acceptance date:
2020-03-03
DOI:
EISSN:
2168-2208
ISSN:
2168-2208
Language:
English
Keywords:
Pubs id:
1091844
Local pid:
pubs:1091844
Deposit date:
2020-03-09

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