Why the study?
Accurate computer analysis of ECG signals is challenging due to high frequency noise and artifacts, which impact the signal-to-noise ratio required by machine learning algorithms in remote healthcare systems.
The Normalized Least Mean Square (NLMS) algorithm provides superior signal-to-noise ratio for ECG de-noising, while Sign LMS offers better computational efficiency.
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NLMS may enhance ECG SNR for remote ML analysis; leaves open optimal filtering choice without clinical validation.
Electrocardiography (ECG) is a common technique for recording the electrical activity of human heart. Accurate computer analysis of ECG signal is challenging as it is exceedingly prone to high frequency noise and various other artifacts due to its low amplitude. In remote heath care systems, computer based high level understanding of ECG signals is performed using advanced machine learning algorithms. The accuracy of these algorithms relies on the Signal-to-Noise-Ratio (SNR) of the input ECG signal. In this paper, we analyse various methods for removing the high frequency noise components from the ECG signal and evaluate the performance of several adaptive filtering algorithms. The result suggest that the Normalized Least Mean Square (NLMS) algorithm achieves high SNR and Sign LMS is computationally efficient.
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Zia-ul-Haque et al. (2019) studied this question.
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