Number of arrhythmia beats determination in Holter electrocardiogram: How many clusters?

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Number of arrhythmia beats determination in Holter electrocardiogram: How many clusters?

Popis výsledku v původním jazyce

Holter signals correspond to long-term electrocardiograph (ECG) registers. Manual inspection of such signals is difficult because of the enormous quantity of beats involved. Throughout the literature several methods of automatically detecting and separating the significant beats using unsupervised learning were proposed. An important part of the unsupervised learning problem is determining the number of constituent clusters which best describe the data. In this paper we concentrate on the problem of thenumber of arrhythmia beats-clusters selection presented in Holter ECG. We apply and compare several criteria for assessing the number of clusters and we show that, with a Gaussian mixture model, the approach is able to select 'an optimal' number of arrhythmia beats and so partition a Holter ECG. The following criteria has been examined: Bayesian selection method, Akaike's information criteria, minimum description length, minimum message length, fuzzy hyper volume, evidence density and .

Název v anglickém jazyce

Number of arrhythmia beats determination in Holter electrocardiogram: How many clusters?

Popis výsledku anglicky

Holter signals correspond to long-term electrocardiograph (ECG) registers. Manual inspection of such signals is difficult because of the enormous quantity of beats involved. Throughout the literature several methods of automatically detecting and separating the significant beats using unsupervised learning were proposed. An important part of the unsupervised learning problem is determining the number of constituent clusters which best describe the data. In this paper we concentrate on the problem of thenumber of arrhythmia beats-clusters selection presented in Holter ECG. We apply and compare several criteria for assessing the number of clusters and we show that, with a Gaussian mixture model, the approach is able to select 'an optimal' number of arrhythmia beats and so partition a Holter ECG. The following criteria has been examined: Bayesian selection method, Akaike's information criteria, minimum description length, minimum message length, fuzzy hyper volume, evidence density and .

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

2003

Kód důvěrnosti údajů

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

Název statě ve sborníku

EMBC 2003

ISBN

0-7803-7790-7

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

2845-2848

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Cancun

Datum konání akce

17. 9. 2003

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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