Publication detail

Adaptive Wavelet Wiener Filtering of ECG Signals

SMITAL, L. VÍTEK, M. KOZUMPLÍK, J. PROVAZNÍK, I.

Original Title

Adaptive Wavelet Wiener Filtering of ECG Signals

Czech Title

Adaptivní vlnkový Wienerův filtr EKG signálů

English Title

Adaptive Wavelet Wiener Filtering of ECG Signals

Type

journal article

Language

en

Original Abstract

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 the classic wavelet Wiener filter.

Czech abstract

Tato studie je zaměřena na potlačování širokopásmových myopotemciálů (EMG) v signálech EKG s využitím vlnkového Wienerova filtrů s pilotním odhadem užitečného signálu. Pro Wienerův filtr i pro odhad užitečného signálu využíváme dyadickou stacionární vlnkovou transformaci (SWT). Naším cílem je nalézt vhodné banky filtrů a zvolit další důležité parametry Wienerova filtru s ohledem na dosažený poměr signálu a šumu (SNR). Testování jsme prováděli na uměle rušených signálech ze standardní CSE databáze, jejíž vzorkovací kmitočet je 500 Hz. Umělé rušení bylo vytvořeno z bílého Gaussova šumu, jehož výkonové spektrum bylo modifikováno tak, aby odpovídalo modelu výkonového spektra EMG signálu. Pro zvýšení filtračního výkonu jsme využili adaptivního nastavení parametrů filtrace podle úrovně vstupního rušení. Jsme schopni zvýšit průměrné SNR cele databáze o asi 10.6 dB. Navržený algoritmus poskytuje lepší výsledky než klasický vlnkový Wienerův filtr.

English abstract

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 the classic wavelet Wiener filter.

Keywords

Broadband myopotentials (EMG) noise, CSE database, ECG signal, Wiener filtering, wavelet transform

RIV year

2013

Released

01.02.2013

Pages from

437

Pages to

445

Pages count

9

URL

BibTex


@article{BUT97333,
  author="Lukáš {Smital} and Martin {Vítek} and Jiří {Kozumplík} and Ivo {Provazník}",
  title="Adaptive Wavelet Wiener Filtering of ECG Signals",
  annote="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 the classic wavelet Wiener filter.",
  chapter="97333",
  number="2",
  volume="60",
  year="2013",
  month="february",
  pages="437--445",
  type="journal article"
}