The greatest problem with recording heart sounds is parasitic noise effects. A reasonable solution to reduce noise can be carried out by minimization of extraneous noises in the vicinity of the patient during recording, in addition to the methods of signal processing that must be effective in noisy environments. Wavelet transform has become an essential tool for many applications, but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) are important key factors to demonstrate the advantages of wavelet denoising. So, selection of optimal mother wavelet with DL is a main challenge to current algorithms. The principal aim of this study was the choice of an appropriate criterion for finding the optimal DL and the optimal mother wavelet function according to four criteria which are: signal-to-noise ratio (SNR), mean square error (MSE), percentage root-mean-square difference (PRD) and the structure similarity index measure (SSIM) for testing the robustness of the proposed algorithm. The proposed method is applied to the PCG signal contaminated with four colored noise types, in addition to the Gaussian noise. The obtained results show the effectiveness of the proposed method in reducing noise from the noisy PCG signals, especially at a low SNR.

中文翻译：

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选择标准使用离散小波变换对心电图（PCG）信号中的平稳和非平稳噪声去除的有效性。

录制心音的最大问题是寄生噪声效应。除了必须在嘈杂环境中有效的信号处理方法外，还可以通过在记录过程中将患者附近的外来噪声减到最小来实现降低噪声的合理解决方案。小波变换已成为许多应用程序中必不可少的工具，但其有效性受主要参数的影响。确定母子波函数和分解水平（DL）是证明子波去噪优势的重要关键因素。因此，选择带有DL的最优母小波是当前算法的主要挑战。这项研究的主要目的是根据以下四个标准选择合适的准则，以找到最佳DL和最佳母小波函数：信噪比（SNR），均方误差（MSE），均方根差百分比（PRD）和结构相似性指标度量（SSIM），用于测试所提出算法的鲁棒性。除高斯噪声外，该方法还适用于被四种彩色噪声污染的PCG信号。获得的结果表明，所提出的方法在减少噪声PCG信号中的噪声方面尤其是在低SNR时是有效的。