Electrocardiogram signal denoising by a new noise variation estimate

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.

Review of noise removal techniques in ECG signals

“…Non‐local means (NLM) has been explored for denoising ECG signals [26 ]. Also, various optimisation techniques like the total variation regularised least‐squares problem or the related fused lasso problem (1DTVD) [27 ], MM technique [28 ], genetic algorithm minimisation of a new noise variation estimate (GAMNVE) [29 ], and so on, help denoise ECG. Some conventional statistical techniques available in literature are principal component analysis and independent component analysis.…”

An efficient ECG signals denoising technique based on the combination of particle swarm optimisation and wavelet transform

“…9 (c) and (d). Another comparison based on two values of SNR input (5 and 10) using records 100 and 103 is made against the following techniques: EMD-ASMF [ 34 ], SDCST [ 35 ], NIWT [ 36 ], 1DTVD [ 37 ], and GAMNVE [ 38 ], as shown in Fig. 9(e)-(f), Fig.…”

“…Furthermore, a variety of optimisation techniques, such as the total variation regularised least squares problem or the associated fused lasso problem (1DTVD) [ 37 ], the genetic algorithm minimisation of a new noise variation estimate (GAMNVE) [ 38 ], and others, assist in denoising the ECG. Principal component analysis and independent component analysis are two common statistical methods described in the literature.…”

ECG Signal Denoising Using Optimized Adaptive Hybrid Filter With Empirical Wavelet Transform

“…It is required to decrease the distortions generated by multiple sources of noise in the case of an ECG. Vargas and Veiga (2020) [9] presented a denoising approach by genetic algorithm minimization of a new noise dissimilarity estimate as a new ECG denoising approach (GAMNVE). In the noisy ECG signal, the GAMNVE approach uses the DWT and processes the wavelet coe cients by reducing a new noise variance estimate.…”