Discriminating Between Explosions and Earthquakes by the SDFA Method

Linhares, Raquel Romes

doi:10.15640/arms.v5n2a1

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…However, ECG signals are often obtained in the presence of noise and analyzing them without the proper noise reduction can lead to misdiagnosis. However, we not always achieve good results with the application of traditional filters in non-stationary signals such as, for example, seismic traces and ECG signals (Linhares 2017(Linhares , 2018.…”

Section: Introductionmentioning

confidence: 86%

Electrocardiogram signal denoising by a new noise variation estimate

Vargas

Veiga

2020

Res. Biomed. Eng.

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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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Section: Introductionmentioning

confidence: 86%