Accurate wavelet thresholding method for ECG signals

Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normali...

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Published inComputers in biology and medicine Vol. 169; p. 107835
Main Authors Yu, Kaimin, Feng, Lei, Chen, Yunfei, Wu, Minfeng, Zhang, Yuanfang, Zhu, Peibin, Chen, Wen, Wu, Qihui, Hao, Jianzhong
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 01.02.2024
Elsevier Limited
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Summary:Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method’s ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy. •A fast wavelet thresholding method based on signal estimation is proposed.•It achieves both accurate threshold and real-time computation.•Its denoising quality surpasses that of other methods.•It can be applied to other wavelet transforms for denoising periodic signals.
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ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2023.107835