Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03099614" target="_blank" >RIV/68407700:21230/04:03099614 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models
Original language description
Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal ECGs. These signals normally include several registers and its duration is up to 48 hours. The principal problem for the cardiologists consists of the manual inspection of the whole holter ECG to find all those beats whose morphology differ from the normal synus rhythm. The later analisys of these arrhythmia beats yields a diagnostic from the pacient's heart condition. Using Hidden Markov Models (HMM) for computer clustering has became a very useful tool for cardiologists avoiding the manual inspection. In this paper we improve the performance of the HMM clustering method introducing a preclustering stage in order to diminish the number of elements to be finally processed and reducing the global computational cost. An experimental comparative study is carried out, utilizing records form the MIT-BIH Arrhythmia database. Finally some results ar.
Czech name
Není k dispozici
Czech description
Není k dispozici

Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Structural, Syntactic, and Statistical Pattern Recognition
ISBN
3-540-22570-6
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
939-947
Publisher name
Springer
Place of publication
Berlin
Event location
Lisbon
Event date
Aug 18, 2004
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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