Why the study?
Do adaptive and wavelet filtering methods improve power line interference cancellation in ECG signals compared to IIR Butterworth notch filters?
Do adaptive and wavelet filtering methods improve power line interference cancellation in ECG signals compared to IIR Butterworth notch filters?
RLS adaptive filters and symlet8 wavelet filtering provide superior reduction of 50 Hz power line noise in ECG signals compared to traditional IIR Butterworth notch filters by avoiding ringing artifacts.
May support RLS and symlet8 wavelet use for ECG denoising; leaves open clinical validation before practice change.
Background: It is essential to reduce these disturbances in ECG signal to improve accuracy and reliability. The bandwidth of the noise overlaps that of wanted signals, so that simple filtering cannot sufficiently enhance the signal to noise ratio. The present paper deals with the digital filtering method to reduce 50 Hz power line noise artifacts in the ECG signal. 4th order Butterworth notch filters(BW=.5 Hz) is used to reduce 50 Hz power line noise interference(PLI) from ECG signals and its performance is compared with Adaptve filters. Method: ECG signal is taken from physionet database. ECG signal (with PLI noise of different frequencies) were processed by Butterworth notch filters of bandwidths of 0.5 Hz. Ringing Artifact is observed in the output. ECG signal (with PLI noise of different frequencies) were processed by Adaptive filters no ringing effect seen. Wavelet filtering applied clean ECG were observed. Result: Performance is compared based on SNR and MSE of Butterworth notch filter and adaptive filters and output of wallet filtering were observed. Conclusion: RLS adaptive filter give better performance as compared to IIR Butterworth and LMS. Clean ECG were seen when filtering using symlet8 wavelet was done.
No takes yet. Share an insight, caveat, or question.
Ahmad et al. (2014) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: