Adaptive overlapping-group sparse denoising for heart sound signals
Adaptive overlapping-group sparse denoising for heart sound signals
复制标题
心音信号的自适应重叠组稀疏去噪
DOI：
10.1016/j.bspc.2017.08.027
复制
发表时间：
2018-02-01
影响因子：
5.1
通讯作者：
Han, Ji-Qing
中科院分区：
文献类型：
--
作者：
Deng, Shi-Wen;Han, Ji-Qing
心音是人体重要的生理信号，在临床听诊中可以提供有价值的诊断信息。然而，HS信号往往受到噪声的污染，噪声会对HS信号的诊断造成不利影响。本文提出了一种基于HS信号一阶差分重叠群稀疏性(OGS)的自适应去噪算法adaOGS。在贝叶斯框架下，推导了adaOGS算法，并将其转化为基于最优-最小化(MM)算法的OGS正则化优化问题。与传统的小波算法相比，该算法不需要预定义基函数，并且可以根据噪声水平自适应地执行。实验结果表明，在较低的噪声水平下，该算法对含噪HS信号的去噪效果优于传统的小波方法如DB10、DB5和BIOR5.5。(C)2017爱思唯尔有限公司。保留所有权利。
The heart sound (HS) is an important physiological signal of the human body and can provide valuable diagnostic information in the clinical auscultation. The HS signal, however, is often contaminated by noise and the noisy HS signal will cause adverse influence of making the diagnosis. In this paper, we proposed an adaptive denoising algorithm, named adaOGS denoising, based on the overlapping group sparsity (OGS) of the first-order difference of the HS signal. Under the Bayesian framework, the adaOGS algorithm is derived and solved as an optimization problem with OGS regularization based on the majorization-minimization (MM) algorithm. Compared with the conventional wavelet method, the proposed algorithm has the advantage that it does not need the predefined base functions and can also be performed in an adaptive way according to the noise level. Moreover, the experimental results show that the proposed algorithm outperforms the conventional wavelet methods such as 'db10', 'db5', and 'bior5.5', for denoising the noisy HS signals in lower noise level. (C) 2017 Elsevier Ltd. All rights reserved.