Garg Nidhi, Ryait Hardeep S, Kumar Amod, Bisht Amandeep
I.K. Gujral Punjab Technical University (PTU), Jalandhar, Punjab-144001, India.
Department of Electronics and Communication, University Institute of Engineering and Technology (UIET), Panjab University (PU), Chandigarh-160023, India.
Biomed Mater Eng. 2018;29(1):53-65. doi: 10.3233/BME-171712.
WPS is a non-invasive method to investigate human health. During signal acquisition, noises are also recorded along with WPS.
Clean WPS with high peak signal to noise ratio is a prerequisite before use in disease diagnosis. Wavelet Transform is a commonly used method in the filtration process. Apart from its extensive use, the appropriate factors for wavelet denoising algorithm is not yet clear in WPS application. The presented work gives an effective approach to select various factors for wavelet denoise algorithm. With the appropriate selection of wavelet and factors, it is possible to reduce noise in WPS.
In this work, all the factors of wavelet denoising are varied successively. Various evaluation parameters such as MSE, PSNR, PRD and Fit Coefficient are used to find out the performance of the wavelet denoised algorithm at every one step.
The results obtained from computerized WPS illustrates that the presented approach can successfully select the mother wavelet and other factors for wavelet denoise algorithm. The selection of db9 as mother wavelet with sure threshold function and single rescaling function using UWT has been a better option for our database.
The empirical results proves that the methodology discussed here could be effective in denoising WPS of any morphological pattern.
小波包谱（WPS）是一种用于研究人体健康的非侵入性方法。在信号采集过程中，WPS信号会伴随着噪声一同被记录下来。
在将WPS用于疾病诊断之前，获得具有高峰值信噪比的干净WPS信号是一个先决条件。小波变换是滤波过程中常用的方法。除了广泛应用外，在WPS应用中，小波去噪算法的合适参数尚不清楚。本文提出了一种为小波去噪算法选择各种参数的有效方法。通过适当选择小波和参数，可以降低WPS中的噪声。
在这项工作中，小波去噪的所有参数依次变化。使用诸如均方误差（MSE）、峰值信噪比（PSNR）、百分比相对畸变（PRD）和拟合系数等各种评估参数来确定小波去噪算法在每一步的性能。
从计算机化的WPS获得的结果表明，本文提出的方法可以成功地为小波去噪算法选择母小波和其他参数。对于我们的数据库来说，选择db9作为母小波，采用确定阈值函数和使用平稳小波变换（UWT）的单重缩放函数是一个更好的选择。
实验结果证明，本文讨论的方法对于去除任何形态模式的WPS噪声都是有效的。
