Key result
A proposed wavelet algorithm at level 8 using the 'dmey' wavelet function was effective for eliminating noise and artifacts in electrocardiogram signals based on power spectrum density.
Why the study?
ECG signals often contain noise and artifacts, and clean signals are needed for more accurate heart disease diagnosis.
A proposed wavelet transform algorithm effectively eliminates noise and artifacts from ECG signals, potentially improving diagnostic accuracy.
May enhance ECG signal quality for diagnosis; leaves open clinical validation before adoption.
Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density.
No takes yet. Share an insight, caveat, or question.
Nguyen et al. (2020) studied Electrocardiogram signal noise and artifacts. Wavelet transform with three stages of decomposition, filter, and reconstruction was evaluated on Signal-to-noise ratio, mean squared error, and power spectral density. A proposed wavelet algorithm at level 8 using the 'dmey' wavelet function was effective for eliminating noise and artifacts in electrocardiogram signals based on power spectrum density.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: