An effective morphological-stabled denoising method for ECG signals using wavelet-based techniques
by Hui Yang; Zhiqiang Wei
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 39, No. 3, 2022

Abstract: Wavelet transform has been identified as an effective denoising method for ECG signals with its advantage of multi-resolution analysis. However, it should be noted that important morphological features, such as peak of the QRS complex, should be retained after denoising for further medical practice. In this paper, an effective morphological-stabled denoising method for ECG signals is proposed though optimal selection of wavelet basis function, designing a new threshold method, optimising decomposition levels and thresholding scheme. When validated in the MIT-BIH Arrhythmia Database, the denoising method achieved mean square error and signal-to-noise value of 0.0146 and 68.6925 respectively, while successfully retained the QRS complex amplitude close to its full amplitude. Also, a total of 23 simulations were carried out to compare our proposed method with other methods. The experimental results indicate that the proposed denoising method can outperform other state-of-the-art wavelet-based methods while remaining stable in morphology.

Online publication date: Mon, 18-Jul-2022

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