Key result
The proposed SC-LNLMS adaptive filter achieved an output SNR varying from 16.53 to 28.56 dB and an MSE less than 0.00000045, outperforming other existing denoising techniques.
Why the study?
ECG signals are susceptible to high-frequency electromyogram noise whose spectrum overlaps with ECG signals, impeding the correct diagnosis of heart diseases.
Population
Noise-free ECG signals from the MIT-BIH Arrhythmia database and noise from the Noise Stress Test database (nstdb)
Comparison
Proposed SC-LNLMS adaptive filter vs other existing techniques
Authors
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May aid EMG noise removal from ECG; leaves open clinical validation and comparison to existing methods.
The SC-LNLMS adaptive filter effectively removes EMG noise from ECG signals, yielding high signal-to-noise ratios and low mean square error without distorting low-amplitude features.
Khiter et al. (2020) studied ECG signal corrupted by EMG noise (n=8). Self correcting leaky normalized least mean square (SC-LNLMS) adaptive filter vs. Other existing denoising techniques (e.g., DWT, EMD, LNLMS) was evaluated on Output Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE). The proposed SC-LNLMS adaptive filter achieved an output SNR varying from 16.53 to 28.56 dB and an MSE less than 0.00000045, outperforming other existing denoising techniques.
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