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Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2004

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Structural, Syntactic, and Statistical Pattern Recognition

ISBN

3-540-22570-6

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

939-947

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Lisbon

Datum konání akce

18. 8. 2004

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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