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Markov models for automated ECG interval analysis

Abstract:
We examine the use of hidden Markov and hidden semi-Markov models for automatically segmenting an electrocardiogram waveform into its constituent waveform features. An undecimated wavelet transform is used to generate an overcomplete representation of the signal that is more appropriate for subsequent modelling. We show that the state durations implicit in a standard hidden Markov model are ill-suited to those of real ECG features, and we investigate the use of hidden semi-Markov models for improved state duration modelling.
Publication status:
Published

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Institute of Biomedical Engineering
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
Neural information processing systems foundation
Volume:
16
Pages:
611-618
Host title:
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16
Publication date:
2004-01-01
ISSN:
1049-5258
Source identifiers:
61551
ISBN:
0262201526
Pubs id:
pubs:61551
UUID:
uuid:4122e4b4-c0ac-4b12-8d34-f380a1cfcedc
Local pid:
pubs:61551
Deposit date:
2012-12-19

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