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Denoising method of ECG signal with power threshold function under wavelet transform and smoothing filter✱

Published: 11 April 2022 Publication History

Abstract

An electrocardiogram (ECG) is an important instrument for doctors to diagnose heart diseases. It is an electrical signal that evolves from the heart and changes over time. It is vulnerable to interference from various low-frequency and high-frequency noise. This paper proposes a new adaptive power threshold function to achieve the denoising of ECG signals. On the basis of wavelet transformation and smooth decomposition, the power threshold function is used to perform an adaptive threshold denoising on the decomposed signal with high frequency noise. The signal is reconstructed from the denoised high frequency components and useful components and coefficients of the remaining layers are set at zero. Taking the ECG signal in the MIT-BIH ECG database as the original data, adding different degrees of Gaussian white noise for experimental analysis, it is proved from the quantitative and qualitative aspects that the proposed method has superiority in removing the noise of the ECG signal compared with the traditional threshold function.

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cover image ACM Conferences
WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
December 2021
541 pages
ISBN:9781450391870
DOI:10.1145/3498851
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 April 2022

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Author Tags

  1. ECG Signal
  2. Power Threshold Function
  3. Smooth Decomposition
  4. Threshold Denoising
  5. Wavelet Transform

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WI-IAT '21
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WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence
December 14 - 17, 2021
VIC, Melbourne, Australia

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