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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
Department:
Oxford, MPLS, Engineering Science, Biomedical Research Centre
Role:
Author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Publisher:
Neural information processing systems foundation
Volume:
16
Pages:
611-618
Publication date:
2004-01-01
ISSN:
1049-5258
URN:
uuid:4122e4b4-c0ac-4b12-8d34-f380a1cfcedc
Source identifiers:
61551
Local pid:
pubs:61551
ISBN:
0-262-20152-6

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