Random suppression of ECG artifacts using a NLSRLMMN-based cascaded noise canceller

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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Publication Details

Published
2024-10-27
DOI
https://doi.org/10.1109/ieeeconf60004.2024.10942836
Primary Topic
Blind Source Separation Techniques
Type
article
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article

Random suppression of ECG artifacts using a NLSRLMMN-based cascaded noise canceller

Azzedine Zerguine, M. Faiz, İzzet Kale
Blind Source Separation Techniques
article

Random suppression of ECG artifacts using a NLSRLMMN-based cascaded noise canceller

Azzedine Zerguine, M. Faiz, İzzet Kale
article en

Abstract

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.

King Fahd University of Petroleum and Minerals (SA), Presidency University (IN), University of Westminster (GB)
Openalex Percentile: Top 67%
Blind Source Separation Techniques
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