Fractionated Electrograms and Rotors Detection in Chronic Atrial Fibrillation Using Model-Based Clustering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00221648" target="_blank" >RIV/68407700:21230/14:00221648 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/EMBC.2014.6943905" target="_blank" >http://dx.doi.org/10.1109/EMBC.2014.6943905</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EMBC.2014.6943905" target="_blank" >10.1109/EMBC.2014.6943905</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fractionated Electrograms and Rotors Detection in Chronic Atrial Fibrillation Using Model-Based Clustering

Popis výsledku v původním jazyce

The identification of atrial fibrillation (AF) substrates is needed to improve ablation therapy guided by electrograms (EGM), although mechanisms that sustain AF are not fully understood. Detection of complex fractionated atrial electrograms (CFAE) is used for this purpose. Nonetheless, efficacy of this method is poor in the case of chronic AF. Recent hypothesis proposes the rotors as fibrillatory substrate. Novel approaches seek to relate CFAE with rotor; nevertheless, such methods are not able to identify the associated substrate. Furthermore, the patterns that characterize CFAE generated by rotors remain unknown. Thus, tracking of rotors is an unsolved issue. In this paper we propose a non-supervised method to find patterns associated with fibrillatory substrates in chronic AF. We extracted two features based on local activation wave detection and one feature based on non-linear dynamics. Gaussian mixture model-based clustering was used to discriminate CFAE patterns. Resulting clust

Název v anglickém jazyce

Fractionated Electrograms and Rotors Detection in Chronic Atrial Fibrillation Using Model-Based Clustering

Popis výsledku anglicky

The identification of atrial fibrillation (AF) substrates is needed to improve ablation therapy guided by electrograms (EGM), although mechanisms that sustain AF are not fully understood. Detection of complex fractionated atrial electrograms (CFAE) is used for this purpose. Nonetheless, efficacy of this method is poor in the case of chronic AF. Recent hypothesis proposes the rotors as fibrillatory substrate. Novel approaches seek to relate CFAE with rotor; nevertheless, such methods are not able to identify the associated substrate. Furthermore, the patterns that characterize CFAE generated by rotors remain unknown. Thus, tracking of rotors is an unsolved issue. In this paper we propose a non-supervised method to find patterns associated with fibrillatory substrates in chronic AF. We extracted two features based on local activation wave detection and one feature based on non-linear dynamics. Gaussian mixture model-based clustering was used to discriminate CFAE patterns. Resulting clust

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

<a href="/cs/project/GPP103%2F11%2FP106" target="_blank" >GPP103/11/P106: Integrace digitálního zpracování obrazu a metod umělé inteligence pro hodnocení komplexity intrakardiálních signálů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

ISBN

978-1-4244-7929-0

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

1579-1582

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Chicago

Datum konání akce

26. 8. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—