Unsupervised Learning of Holter ECG signals using HMM optimized by simulated annealing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03099084" target="_blank" >RIV/68407700:21230/04:03099084 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Unsupervised Learning of Holter ECG signals using HMM optimized by simulated annealing
Original language description
We present a unsupervised learning algorithm based on continuous Hidden Markov Models (HMM) to automatically classify Holter signals based on their morphology. Our proposed method automatically detect and separate the significant beats by means of hierarchical clustering scheme. Due to the convergence and numeric problems of a classical local optimization technique, we have implemented a novel approach for the global training of HMM by simulated annealing
Czech name
Není k dispozici
Czech description
Není k dispozici
Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Analysis of Biomedical Signals and Images
ISBN
80-214-2633-0
ISSN
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e-ISSN
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Number of pages
3
Pages from-to
60-62
Publisher name
VUTIUM Press
Place of publication
Brno
Event location
Brno
Event date
Jun 23, 2004
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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