### Abstract

A wavelet transform (WT) based filtering of the High Resolution Electrocardiogram is proposed in this study. The overcomplete time-scale decomposition of the row signal is achieved in the first step by convolution with the quadratic spline wavelet. The small scale data are passed by the filtering algorithm at locations where the wavelet coefficients correlation at adjacent scales is high and suppressed if the correlation is small. The filtered signal is reconstructed from the set of filtered scales through the inverse WT. A comparison with the Wiener filter proved that the wavelet filtering performed better in passing the useful high-frequency data from the QRS terminal part. The denoising results obtained on real signals were similar with 0.6 μV root-mean-square noise endpoint signal averaged ECG.

Original language | English (US) |
---|---|

Title of host publication | Computers in Cardiology |

Pages | 637-640 |

Number of pages | 4 |

Volume | 0 |

Edition | 0 |

State | Published - 1996 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Software
- Cardiology and Cardiovascular Medicine

### Cite this

*Computers in Cardiology*(0 ed., Vol. 0, pp. 637-640)

**Selective noise filtering of high resolution ECG through wavelet transform.** / Bezerianos, A.; Popescu, M.; Laskaris, N.; Manolis, A.; Hiladakis, I.; Stathopoulos, C.; Cristea, P.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Computers in Cardiology.*0 edn, vol. 0, pp. 637-640.

}

TY - CHAP

T1 - Selective noise filtering of high resolution ECG through wavelet transform

AU - Bezerianos, A.

AU - Popescu, M.

AU - Laskaris, N.

AU - Manolis, A.

AU - Hiladakis, I.

AU - Stathopoulos, C.

AU - Cristea, P.

PY - 1996

Y1 - 1996

N2 - A wavelet transform (WT) based filtering of the High Resolution Electrocardiogram is proposed in this study. The overcomplete time-scale decomposition of the row signal is achieved in the first step by convolution with the quadratic spline wavelet. The small scale data are passed by the filtering algorithm at locations where the wavelet coefficients correlation at adjacent scales is high and suppressed if the correlation is small. The filtered signal is reconstructed from the set of filtered scales through the inverse WT. A comparison with the Wiener filter proved that the wavelet filtering performed better in passing the useful high-frequency data from the QRS terminal part. The denoising results obtained on real signals were similar with 0.6 μV root-mean-square noise endpoint signal averaged ECG.

AB - A wavelet transform (WT) based filtering of the High Resolution Electrocardiogram is proposed in this study. The overcomplete time-scale decomposition of the row signal is achieved in the first step by convolution with the quadratic spline wavelet. The small scale data are passed by the filtering algorithm at locations where the wavelet coefficients correlation at adjacent scales is high and suppressed if the correlation is small. The filtered signal is reconstructed from the set of filtered scales through the inverse WT. A comparison with the Wiener filter proved that the wavelet filtering performed better in passing the useful high-frequency data from the QRS terminal part. The denoising results obtained on real signals were similar with 0.6 μV root-mean-square noise endpoint signal averaged ECG.

UR - http://www.scopus.com/inward/record.url?scp=0030380988&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030380988&partnerID=8YFLogxK

M3 - Chapter

AN - SCOPUS:0030380988

VL - 0

SP - 637

EP - 640

BT - Computers in Cardiology

ER -