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Distinguishing Between Supply Ischaemic and Non-Supply Ischaemic ST Events using a Relevance Vector Machine

Abstract:

In this paper, we apply a sparse Bayesian learning algorithm called the Relevance Vector Machine (RVM) which was used to classify the 1126 ischaemic ST events and 1126 non-supply ischaemic ST events in the Long Term ST Database as supply or non-supply ST episodes. A Genetic Algorithm (GA) method was used to identify which of the extracted features used as input to the RVM were the most important with respect to the model's performance. The GA indicated that 9 of the 35 extracted features were...

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Publication status:
Published

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science, Biomedical Research Centre
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Journal:
2011 COMPUTING IN CARDIOLOGY
Volume:
38
Pages:
633-636
Publication date:
2011-01-01
EISSN:
2325-887X
ISSN:
2325-8861
URN:
uuid:86cb4aeb-43e8-44a4-a738-07bed5f98473
Source identifiers:
329446
Local pid:
pubs:329446
Language:
English

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