Key result
A modified wavelet design method effectively removed high-frequency noise, enhanced P and T wave characteristics, and improved signal-to-noise ratio compared with db4 and sym4 wavelets.
Why the study?
Due to frequency overlap between EMG and ECG, feeble ECG characteristics risk being weakened during standard wavelet noise filtering.
Does a modified wavelet-based ECG denoising method improve signal-to-noise ratio and reduce mean square error compared to db4 and sym4 wavelets in clinical ECG data?
Does a modified wavelet-based ECG denoising method improve signal-to-noise ratio and reduce mean square error compared to db4 and sym4 wavelets in clinical ECG data?
A novel modified wavelet design effectively removes high-frequency noise from ECG signals while preserving and enhancing weak features such as P waves, T waves, and atrial fibrillation signals.
May improve mobile ECG clarity; leaves open whether denoising enhances diagnostic accuracy or outcomes.
Purpose: Wavelet denoising is one of the denoising methods commonly used for ECG signals. However, due to the frequency overlap between the EMG and ECG, the feeble characteristics of ECG signals exists the risk of being weakened in the process of filtering noise. This paper presents a method of modified wavelet design and applies it to the denoising of ECG signals.Materials and methods: The optimized filter coefficients are obtained by approximating the amplitude-frequency response of the ideal filter, and the wavelet is constructed with the optimized filter coefficients. The algorithm is tested by clinical ECG data.Results: The results show that the proposed denoising method can remove the high-frequency noise effectively and enhance the characteristic information of P waves and T waves, and retain the characteristic information of the atrial fibrillation signals simultaneously. Compared with db4 and sym4 wavelets, the proposed wavelet can improve the signal to noise ratio and reduce the mean square error effectively at the same time.Conclusion: The modified wavelet design method proposed in this paper can effectively remove high-frequency noise while retaining and enhancing weak features. It provides a theoretical guidance for the de-noising of ECG signals in mobile medicine and also provides a way for other types of weak feature signal denoising.
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Wang et al. (2019) studied ECG signals. Modified wavelet design method vs. db4 and sym4 wavelets was evaluated on Signal to noise ratio and mean square error. A modified wavelet design method effectively removed high-frequency noise, enhanced P and T wave characteristics, and improved signal-to-noise ratio compared with db4 and sym4 wavelets.
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