Conference item
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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Bibliographic Details
- 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
Item Description
- Pubs id:
-
pubs:61551
- UUID:
-
uuid:4122e4b4-c0ac-4b12-8d34-f380a1cfcedc
- Local pid:
- pubs:61551
- Deposit date:
- 2012-12-19
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- Copyright date:
- 2004
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