Key result
Butterworth filtering yields top SNR while zero-phase low-pass filtering achieves ~100% accuracy in ECG denoising.
Why the study?
ECG signals are affected by noise during data acquisition, necessitating effective denoising algorithms for accurate cardiac diagnostics.
Population
ECG data samples from the MIT-BIH database
Comparison
Denoising algorithms using IIR filters (Butterworth, Elliptic, Chebyshev I and II) and FIR filters (Zero-phase low pass, Hamming window, rectangular window)
Design
Simulation study using MATLAB and Wavelet toolbox
Authors
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Butterworth and zero-phase low pass filters provide optimal SNR and accuracy, respectively, for denoising ECG signals.
Butterworth and zero-phase low pass filters provide optimal SNR and accuracy, respectively, for denoising ECG signals.
Bhogeshwar et al. (2014) studied Cardiac diseases (ECG signal denoising). IIR and FIR filters (Butterworth, Elliptic, Chebyshev, Zero-phase low pass, Hamming, rectangular window) vs. Comparison among different filters was evaluated on Signal-to-Noise Ratio (SNR), error and accuracy. In a simulation study of ECG signal denoising, the Butterworth filter achieved the highest Signal-to-Noise Ratio (49.03), while the zero-phase low pass filter achieved the highest accuracy (99.58%).
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