Key result
NLSRLMMN algorithm improves ECG signal-to-noise ratio and mean square error vs conventional LMS.
Why the study?
ECG signal artifacts are normally removed in a particular sequence starting with baseline wander, prompting evaluation of removing multiple artifacts randomly without following a set sequence.
A novel NLSRLMMN-based algorithm improves ECG signal quality by effectively removing multiple artifacts randomly, outperforming conventional LMS methods.
May advance ECG denoising research; hypothesis-generating pending clinical validation.
Normally, the electrocardiogram (ECG) signal artifacts are removed in a particular sequence starting with baseline wander removal at the beginning. In this work, a methodology has been proposed using a novel algorithm called the Normalized Leaky Sign Regressor Least Mean Mixed Norm (NLSRLMMN) algorithm to remove multiple ECG artifacts such as baseline wander, motion artifacts, muscle artifacts, and power line interference randomly without following a particular sequence. Through rigorous simulations we have shown that if we remove multiple ECG artifacts randomly using our proposed approach then an improvement in signal to noise ratio and mean square error is observed as opposed to a conventional method that employs the Least Mean Square (LMS) algorithm in all the cascaded adaptive noise canceller stages.
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Faiz et al. (2024) studied ECG artifacts. Normalized Leaky Sign Regressor Least Mean Mixed Norm (NLSRLMMN) algorithm vs. Least Mean Square (LMS) algorithm was evaluated on Signal to noise ratio and mean square error. Random removal of multiple ECG artifacts using the NLSRLMMN algorithm improved signal to noise ratio and mean square error compared to a conventional LMS algorithm.
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