Key result
Empirical mode decomposition algorithm shows promise over existing methods for removing high-frequency ECG noise.
Why the study?
ECG signals are often contaminated by various noises during acquisition and transmission, necessitating effective denoising methods for accurate heart condition diagnosis.
Answers are grounded in this paper and cite their sources.
A modified empirical mode decomposition and moving average filter approach effectively denoises ECG signals by removing high-frequency artifacts.
Electrocardiogram (ECG) signals are the reflections of the heart’s condition and hence any abnormal heart condition will also appear as irregularities in the ECG signal. Different noises that get embedded with ECG signal during its acquisition and transmission are powerline interference, baseline wandering, electromyogram noises, motion artefacts, and channel noises. Hence, for the proper diagnosis of the heart, the ECG signals must be free of noises. In this work, denoising of the ECG signal is the major objective and technique used for this purpose is based on the empirical mode decomposition (EMD) due to its adaptive and data driven nature suitable for any non-stationary signal. The high frequency noises have been considered for removal that includes electromyogram noises (MA), motion artefacts (EM), as well as White Gaussian noises. The proposed algorithm is compared with the existing algorithms and promising results are obtained which justifies the validation of the proposed method.
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Jha et al. (2014) studied ECG signal noise. Empirical mode decomposition (EMD) and moving average filter vs. Existing algorithms was evaluated on Denoising of the ECG signal (removal of high frequency noises). An ECG signal denoising algorithm based on empirical mode decomposition and moving average filter yielded promising results compared with existing algorithms for removing high frequency noises.
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