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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F13%3APU102076%21RIV14-GA0-26220___
rdf:type
n9:Vysledek skos:Concept
rdfs:seeAlso
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6357230&contentType=Journals+%26+Magazines&searchField%3DSearch_All%26queryText%3Dsmital
dcterms:description
In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than t In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than t
dcterms:title
Adaptive Wavelet Wiener Filtering of ECG Signals Adaptive Wavelet Wiener Filtering of ECG Signals
skos:prefLabel
Adaptive Wavelet Wiener Filtering of ECG Signals Adaptive Wavelet Wiener Filtering of ECG Signals
skos:notation
RIV/00216305:26220/13:PU102076!RIV14-GA0-26220___
n9:predkladatel
n13:orjk%3A26220
n3:aktivita
n16:S n16:P
n3:aktivity
P(ED1.100/02/0123), P(GD102/09/H083), S
n3:cisloPeriodika
2
n3:dodaniDat
n19:2014
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n18:J
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
59502
n3:idVysledku
RIV/00216305:26220/13:PU102076
n3:jazykVysledku
n6:eng
n3:klicovaSlova
Broadband myopotentials (EMG) noise, CSE database, ECG signal, Wiener filtering, wavelet transform
n3:klicoveSlovo
n11:Broadband%20myopotentials%20%28EMG%29%20noise n11:ECG%20signal n11:Wiener%20filtering n11:CSE%20database n11:wavelet%20transform
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[AE15CF38A258]
n3:nazevZdroje
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
n3:obor
n8:JA
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
4
n3:projekt
n5:GD102%2F09%2FH083 n5:ED1.100%2F02%2F0123
n3:rokUplatneniVysledku
n19:2013
n3:svazekPeriodika
60
n3:tvurceVysledku
Provazník, Ivo Smital, Lukáš Kozumplík, Jiří Vítek, Martin
s:issn
0018-9294
s:numberOfPages
9
n12:organizacniJednotka
26220