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首页> 外文期刊>Sadhana: Academy Proceedings in Engineering Science >Electrocardiogram de-noising based on forward wavelet transform translation invariant application in bionic wavelet domain
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Electrocardiogram de-noising based on forward wavelet transform translation invariant application in bionic wavelet domain

机译:基于前向小波变换平移不变的仿生小波域心电图降噪

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摘要

In this paper, we propose a new technique of Electrocardiogram (ECG) signal de-noising based on thresholding of the coefficients obtained from the application of the Forward Wavelet Transform Translation Invariant (FWT_TI) to each Bionic Wavelet coefficient. The De-noise De-noised ECG is obtained from the application of the inverse of BWT (BWT~(-1)) to the de-noise de-noised bionic wavelet coefficients. For evaluating this new proposed de-noising technique, we have compared it to a thresholding technique in the FWT_TI domain. Preliminary tests of the application of the two de-noising techniques were constructed on a number of ECG signals taken from MIT-BIH database. The obtained results from Signal to Noise Ratio (SNR) and Mean Square Error (MSE) computations showed that our proposed de-noising technique outperforms the second technique. We have also compared the proposed technique to the thresholding technique in the bionic wavelet domain and this comparison was performed by SNR improvement computing. The obtained results from this evaluation showed that the proposed technique also outperforms the de-noising technique based on bionic wavelet coefficients thresholding.
机译:在本文中,我们基于对每个仿生小波系数应用正向小波变换平移不变性(FWT_TI)获得的系数的阈值,提出了一种心电图(ECG)信号降噪的新技术。通过将BWT的逆(BWT〜(-1))应用于去噪去噪仿生小波系数,可以得到去噪去噪ECG。为了评估这种新提出的降噪技术,我们将其与FWT_TI域中的阈值技术进行了比较。两种降噪技术应用的初步测试是基于从MIT-BIH数据库获取的许多ECG信号构建的。从信噪比(SNR)和均方误差(MSE)计算获得的结果表明,我们提出的降噪技术优于第二种技术。我们还将拟议的技术与仿生小波域中的阈值技术进行了比较,并通过SNR改进计算进行了此比较。通过该评估获得的结果表明,所提出的技术也优于基于仿生小波系数阈值的去噪技术。

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