"60" . . . . "RIV/00216305:26220/13:PU102076!RIV14-GA0-26220___" . . . "0018-9294" . "2" . . . "26220" . . "4"^^ . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . "Adaptive Wavelet Wiener Filtering of ECG Signals" . "4"^^ . "Provazn\u00EDk, Ivo" . . . "[AE15CF38A258]" . "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"@en . . "RIV/00216305:26220/13:PU102076" . "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6357230&contentType=Journals+%26+Magazines&searchField%3DSearch_All%26queryText%3Dsmital" . . . . "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" . "P(ED1.100/02/0123), P(GD102/09/H083), S" . . . . "Smital, Luk\u00E1\u0161" . . "Kozumpl\u00EDk, Ji\u0159\u00ED" . "Adaptive Wavelet Wiener Filtering of ECG Signals"@en . "Broadband myopotentials (EMG) noise, CSE database, ECG signal, Wiener filtering, wavelet transform"@en . . "IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING" . . . "59502" . "V\u00EDtek, Martin" . "9"^^ . "Adaptive Wavelet Wiener Filtering of ECG Signals" . . "Adaptive Wavelet Wiener Filtering of ECG Signals"@en . .