Why the study?
Mean squared error criteria are suboptimal for ECG denoising because they assume Gaussian noise and stationary signals, unlike non-stationary ECG signals with non-Gaussian noise.
Does the MEE-ANC algorithm improve signal-to-noise ratio in ECG denoising compared to the LMS-ANC algorithm?
Does the MEE-ANC algorithm improve signal-to-noise ratio in ECG denoising compared to the LMS-ANC algorithm?
An adaptive noise cancellation method based on minimum error entropy significantly improves signal-to-noise ratio in ECG denoising compared to conventional mean squared error-based methods.
Entropy-based ECG denoising may outperform MSE for non-Gaussian noise; leaves open clinical validation before practice change.
In this paper, we use an adaptive method that conforms to the error entropy criterion in order to eliminate noise from cardiac signals electrocardiogram (ECG). In previous works, the mean squared error (MSE) criterion has been used to adaptive noise cancelation of ECG signals, which only has the ability to minimize the second moment of error. The MSE criterion only works optimally on systems with Gaussian noise and stationary signals, so this is not suitable for ECG signals that are non-stationary and have non-Gaussian noise. In contrast, the use of error entropy-based algorithms like minimum error entropy (MEE) is very useful in ECG noise cancelation. The results of the proposed algorithm indicate a significant advantage in terms of signal-to-noise ratio (SNR) and convergence value compared to the algorithm based on MSE criteria.
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Saad et al. (2023) studied this question.
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