Key result
The sign-sign LMS adaptive filter outperformed other algorithms in denoising ECG signals, achieving a mean square error of 0.0253 and a signal-to-noise ratio of 5.327.
Population
48 annotated ECG records from 46 subjects from the MIT-BIH arrhythmia database, sampled at 360 Hz.
Comparison
Adaptive filtering algorithms applied to noisy… vs Comparison among different adaptive filtering…
Authors
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ECG denoising methods may aid signal quality in practice; leaves open optimal algorithm selection for clinical validation.
The sign-sign LMS adaptive filter demonstrates superior performance in removing various types of noise from ECG signals compared to other adaptive algorithms.
Sahu et al. (2017) studied ECG signal noise (n=46). Sign-sign LMS (SSLMS) adaptive filter vs. Other adaptive filters (LMS, NLMS, RLS, SLMS, QDRLS) was evaluated on Signal to noise ratio (SNR), mean square error (MSE), normalized mean square error (NMSE), and percentage root square difference (%PRD). The sign-sign LMS adaptive filter outperformed other algorithms in denoising ECG signals, achieving a mean square error of 0.0253 and a signal-to-noise ratio of 5.327.
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