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A review on ECG filtering techniques for rhythm analysis

A review on ECG filtering techniques for rhythm analysis PurposeElectrocardiogram (ECG) signal recording is a challenging task in the field of biomedical engineering. ECG is the cardiac recording of systematic electrical activity arising from the electro-physiological rhythm of the heart muscle. But, during processing, the ECG signal is contaminated with different types of noise in the medical environment. An immense task is the separation of the preferred signal from noises caused by artifacts like muscle noise, power line interference (PLI), baseline wandering (BW), and motion artifacts (MA). Hence, our paper focuses on 50 Hz PLI which is a major artifact/noise affecting the recorded ECG signal.MethodsThis paper comprehensively reviews fundamental concepts of different denoising techniques. Some of the pioneers’ works are also concisely explained in the paper. Further, in this work, comparative analysis is carried out using notch filter, adaptive filter, discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for filtering 50 Hz PLI noise.ResultsA considerable improvement in signal-to-noise ratio (SNR) can be observed from the results when compared with SNR input and SNR output values. Performance comparisons of all the four techniques are also analyzed based on variations in noise frequency. The simulations were carried out in the environment of MATLAB 2019b®.ConclusionThis work epitomizes the significance of our quantitative evaluation, in which adaptive filters are found to perform better with respect to the SNR, whereas DWT performs better with assessment of mean square error (MSE). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

A review on ECG filtering techniques for rhythm analysis

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Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2020
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-020-00057-9
Publisher site
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Abstract

PurposeElectrocardiogram (ECG) signal recording is a challenging task in the field of biomedical engineering. ECG is the cardiac recording of systematic electrical activity arising from the electro-physiological rhythm of the heart muscle. But, during processing, the ECG signal is contaminated with different types of noise in the medical environment. An immense task is the separation of the preferred signal from noises caused by artifacts like muscle noise, power line interference (PLI), baseline wandering (BW), and motion artifacts (MA). Hence, our paper focuses on 50 Hz PLI which is a major artifact/noise affecting the recorded ECG signal.MethodsThis paper comprehensively reviews fundamental concepts of different denoising techniques. Some of the pioneers’ works are also concisely explained in the paper. Further, in this work, comparative analysis is carried out using notch filter, adaptive filter, discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for filtering 50 Hz PLI noise.ResultsA considerable improvement in signal-to-noise ratio (SNR) can be observed from the results when compared with SNR input and SNR output values. Performance comparisons of all the four techniques are also analyzed based on variations in noise frequency. The simulations were carried out in the environment of MATLAB 2019b®.ConclusionThis work epitomizes the significance of our quantitative evaluation, in which adaptive filters are found to perform better with respect to the SNR, whereas DWT performs better with assessment of mean square error (MSE).

Journal

Research on Biomedical Engineering – Springer Journals

Published: Jun 19, 2020

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