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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F12%3A00193450%21RIV13-MSM-21230___
rdf:type
n6:Vysledek skos:Concept
dcterms:description
Though atrial electrogram (A-EGM) signal processing plays more and more important role in research that helps physicians to ease and shorten radiofrequency ablation (RFA) procedure of atrial fibrillation (AF), feature extraction algorithms are still not well and clearly understood and described in contemporary literature. Conversely different methods of A-EGM evaluation are compared and published frequently, but often based on these feature extraction algorithms that are not so well published. This paper is aimed to start to put the light on the basics that precedes using of features for A-EGM evaluation. Basic wavelet signal processing tools are used to preprocess and extract simple but (as proven in published research) valuable features of A-EGM signal that was measured during RFA of AF. This approach was proven and it helps to classify complexity of A-EGM signals. More detailed view on the extraction process is given here to enable an easy and reproducible usage of these A-EGM processing and feature extraction algorithms so that future research of A-EGM complexity description can be on the firm base and fully reproducible while evaluating RFA signals for AF treatment. Though atrial electrogram (A-EGM) signal processing plays more and more important role in research that helps physicians to ease and shorten radiofrequency ablation (RFA) procedure of atrial fibrillation (AF), feature extraction algorithms are still not well and clearly understood and described in contemporary literature. Conversely different methods of A-EGM evaluation are compared and published frequently, but often based on these feature extraction algorithms that are not so well published. This paper is aimed to start to put the light on the basics that precedes using of features for A-EGM evaluation. Basic wavelet signal processing tools are used to preprocess and extract simple but (as proven in published research) valuable features of A-EGM signal that was measured during RFA of AF. This approach was proven and it helps to classify complexity of A-EGM signals. More detailed view on the extraction process is given here to enable an easy and reproducible usage of these A-EGM processing and feature extraction algorithms so that future research of A-EGM complexity description can be on the firm base and fully reproducible while evaluating RFA signals for AF treatment.
dcterms:title
A-EGM Features Extraction Using Wavelet Signal Processing A-EGM Features Extraction Using Wavelet Signal Processing
skos:prefLabel
A-EGM Features Extraction Using Wavelet Signal Processing A-EGM Features Extraction Using Wavelet Signal Processing
skos:notation
RIV/68407700:21230/12:00193450!RIV13-MSM-21230___
n6:predkladatel
n15:orjk%3A21230
n3:aktivita
n20:Z n20:P
n3:aktivity
P(GPP103/11/P106), Z(MSM6840770012)
n3:dodaniDat
n13:2013
n3:domaciTvurceVysledku
n17:1205447
n3:druhVysledku
n8:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n23:predkladatel
n3:idSjednocenehoVysledku
121235
n3:idVysledku
RIV/68407700:21230/12:00193450
n3:jazykVysledku
n9:eng
n3:klicovaSlova
Atrial Fibrillation; Intracardial Signals; Complex Fractionated Atrial Electrograms; Feature Extraction; Signal Processing
n3:klicoveSlovo
n4:Signal%20Processing n4:Feature%20Extraction n4:Atrial%20Fibrillation n4:Intracardial%20Signals n4:Complex%20Fractionated%20Atrial%20Electrograms
n3:kontrolniKodProRIV
[E4A07BE78DB8]
n3:mistoKonaniAkce
Beijing ( Peking)
n3:mistoVydani
Heidelberg
n3:nazevZdroje
IFMBE Proceedings: World Congress on Medical Physics and Biomedical Engineering
n3:obor
n18:JC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n21:GPP103%2F11%2FP106
n3:rokUplatneniVysledku
n13:2012
n3:tvurceVysledku
Křemen, Václav
n3:typAkce
n22:WRD
n3:zahajeniAkce
2012-05-26+02:00
n3:zamer
n10:MSM6840770012
s:issn
1680-0737
s:numberOfPages
4
n14:hasPublisher
Springer-Verlag
n12:isbn
978-3-642-29304-7
n16:organizacniJednotka
21230