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Electrocardiogram signal denoising by a new noise variation estimate

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Abstract

Purpose

Separating or eliminating the noise from a biomedical signal is what allows the accuracy of a diagnosis. In particular, in the case of an electrocardiogram (ECG), it is necessary to reduce the distortions caused by several sources of noise. In this paper, we propose a new ECG denoising method called by noise reduction by genetic algorithm minimization of a new noise variation estimate (GAMNVE).

Methods

The GAMNVE method applies the discrete wavelet transform (DWT) in the noisy ECG signal and processes the wavelet coefficients by the minimization of a new noise variance estimate. This minimization was made by genetic algorithm. For the simulations, we consider eight real ECG signal corrupted by additive white Gaussian noise (AWGN), power line interference (PLI), and muscle artifact (MA).

Results

We compare the GAMNVE method with five well-known denoising methods. The simulations results show that the GAMNVE method presents a better performance for the considered cases.

Conclusion

Simulations have demonstrated that the GAMNVE method can be applied in noisy ECG signals with superior performance than other methods established in the literature.

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Acknowledgments

This work was supported in part by Coordination for the Improvement of Higher Education Personnel (CAPES).

Funding

This study was funded by Coordination for the Improvement of Higher Education Personnel (CAPES) (grant number 1181XPOS070).

Author information

Correspondence to Regis Nunes Vargas.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

The authors declare that they have no conflict of interest.

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Vargas, R.N., Veiga, A.C.P. Electrocardiogram signal denoising by a new noise variation estimate. Res. Biomed. Eng. 36, 13–20 (2020). https://doi.org/10.1007/s42600-019-00033-y

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Keywords

  • Electrocardiogram
  • Denoising
  • Wavelets
  • Genetic algorithms