Comparison of Several Classifiers to Evaluate Endocardial Electrograms Fractionation in Human

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00189630" target="_blank" >RIV/68407700:21230/09:00189630 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/09:00189630

Výsledek na webu

<a href="http://dx.doi.org/10.1109/IEMBS.2009.5335161" target="_blank" >http://dx.doi.org/10.1109/IEMBS.2009.5335161</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IEMBS.2009.5335161" target="_blank" >10.1109/IEMBS.2009.5335161</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Several Classifiers to Evaluate Endocardial Electrograms Fractionation in Human

Popis výsledku v původním jazyce

Complex fractionated atrial electrograms (CFAEs) may represent the electrophysiological substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies. A novel algorithm for automated description of atrial electrograms (A-EGMs) fractionation based on wavelet transform and several statistical pattern recognition methods was proposed and new methodology of A-EGM processing was designed and tested. The algorithms for A-EGM classification were developed using normal density based classifiers, linear and high degree polynomial classifiers, nearest mean scaled classifiers, nonlinear classifiers, neural networks and j48. All classifiers were compared and testedusing a representative set of 1.5 s A-EGMs (n = 68) ranked by 3 independent experts 100% coincidentialy into 4 classes of fractionation: 1 - organized atrial activity; 2 - mild; 3 - intermediate; 4 - high degree of fractionation. Feature

Název v anglickém jazyce

Comparison of Several Classifiers to Evaluate Endocardial Electrograms Fractionation in Human

Popis výsledku anglicky

Complex fractionated atrial electrograms (CFAEs) may represent the electrophysiological substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies. A novel algorithm for automated description of atrial electrograms (A-EGMs) fractionation based on wavelet transform and several statistical pattern recognition methods was proposed and new methodology of A-EGM processing was designed and tested. The algorithms for A-EGM classification were developed using normal density based classifiers, linear and high degree polynomial classifiers, nearest mean scaled classifiers, nonlinear classifiers, neural networks and j48. All classifiers were compared and testedusing a representative set of 1.5 s A-EGMs (n = 68) ranked by 3 independent experts 100% coincidentialy into 4 classes of fractionation: 1 - organized atrial activity; 2 - mild; 3 - intermediate; 4 - high degree of fractionation. Feature

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society

ISBN

978-1-4244-3295-0

ISSN

1557-170X

e-ISSN

—

Počet stran výsledku

4

Strana od-do

2502-2505

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Minneapolis

Datum konání akce

2. 9. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000280543601363