Key result
Various ECG signal denoising methods, including empirical mode decomposition, deep learning, and discrete wavelet transform, are essential pre-processing steps to effectively remove artifacts and noise.
Why the study?
ECG signals are contaminated by various noises and artifacts in real-world applications, making separating the desired signal a complicated task.
This review summarizes computational methods for removing noise from ECG signals, which is a crucial pre-processing step for accurate cardiac monitoring.
An electrocardiogram (ECG) quantifies the electrical activity of the heart to screen for different heart diseases, although it can be impacted by noise. ECG signal filtering is a crucial pre-processing step that reduces noise and emphasizes the characteristic waves in ECG data. In real-world applications, the ECG signal is contaminated by different types of noise. Separating the desired signal from noises produced by artifacts such as muscle noise, power line interference (PLI), baseline wandering (BW), and motion artifacts (MA) is a complicated task. In this paper, a quick review of various ECG signal denoising methods is introduced.
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Ahmed et al. (2022) conducted a review in Electrocardiogram (ECG) signal noise. ECG signal filtering approaches was evaluated. Various ECG signal denoising methods, including empirical mode decomposition, deep learning, and discrete wavelet transform, are essential pre-processing steps to effectively remove artifacts and noise.
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