首页 > 过刊浏览>2017年第31卷第3期 >383-388. DOI:10.13382/j.jemi.2017.03.008
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基于小波收缩的心音降噪最优化分析
作者:
作者单位:

1. 天津市生物医学检测技术及仪器重点实验室天津300072;2. 天津天堰科技股份有限公司天津300384

中图分类号:

R318.11；TN911.71

基金项目:

国家重大仪器专项（2012YQ060165）、天津市科技支撑重点项目（14ZCZDSF00005）资助


Optimization analysis of noise reduction in heart sound based on wavelet shrinkage
Author:
Affiliation:

1. Tianjin Key Laboratory of Biomedical & Instruments,Tianjin University, Tianjin 30072, China;2. Tellyes Scientific Co. Ltd., Tianjin 300384, China

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    摘要:

    基于小波收缩技术提出一种最优化降噪方案用于去除心音信号噪声。依据频带相似匹配原则，分析心音信号频率特征和Haar、Daubechies、Symlets和Coiflets正交小波的特性，基于分析结果选取了Coif5小波并确定最优小波包进行分解重构。另外，提出一种光滑连续的自适应弹性阈值函数，能够克服硬阈值函数间断点问题，并基于4种阈值规则定量评价了其在不同信噪比下的降噪效果。仿真结果表明，当信噪比小于50 dB时，本优化方案配合启发式阈值规则能保留充足的心音细节信息，同时有效去除噪音。

    Abstract:

    A denoising scheme based on wavelet shrinkage technique is proposed to remove the noise in the heart sound signal. Firstly, the characteristics of heart sound signal frequency were analyzed and Haar, Daubechies, Symlets and Coiflets orthogonal wavelets were studied for contrast in accordance with the principle of frequency band similarity matching. Based on the statistical results, Coif5 wavelet was chosen for the decomposition and reconstruction of heart sound signal. Besides, a smooth and continuous adaptive elastic threshold function was designed for wavelet shrinkage, which can overcome the problem of discontinuous hard threshold function. The noise reduction effects were compared under four threshold rules. The simulation results show that, when the SNR is less than 50 dB, the optimization scheme with Heursure threshold rule can retain sufficient heart sound detail information, while effectively removing noise.

    参考文献
    [1]刘学,李婷,孙宸,等. 基于小波变换的心音信号去噪方法[J]. 科技信息,2013(2):189190. LIU X,LI T,SUN CH,et al. Denoising method of heart sound signal based on wavelet transform[J].Science & Technology Information, 2013(2):189 190.
    [2]LIU F,WANG Y T,WANG Y X. Research and implementation of heart sound denoising[C]. Proceedings of 2012 International Conference on Solid State Devices and Materials Science(SSDMS 2012 V25), 2012: 777785.
    [3]李红延,周云龙,田峰,等. 一种新的小波自适应阈值函数振动信号去噪算法[J]. 仪器仪表学报,2015,36(10):22002206. LI H Y,ZHOU Y L,TIAN F,et al. Waveletbased vibration signal denoising algorithm with a new adaptive threshold function[J]. Chinese Journal of Scientific Instrument, 2015, 36(10):22002206.
    [4]黄政钦,孙静,张丽娜,等. 心音、心电采集系统设计与信号预处理[J]. 电子测量技术,2014,37(9): 117 121,131. HUANG ZH Q,SUN J,ZHANG L N,et al. ECG and heart sound acquisition system and signal preprocessing[J]. Electronic Measurement Technology, 2014, 37(9):117121,131.
    [5]YUENYONG S, NISHIHARA A, KONGPRAWECHNON W. A framework for automatic heart sound analysis without segmentation[J]. BioMedical Engineering Online, 2011,10 (1) :12
    [6]郑蕾. 基于小波变换的心音信号分析方法的研究[D].兰州:兰州理工大学,2010. ZHENG L. The researches of heart sound signal on the basis of wavelet[D]. Lanzhou: Lanzhou University of Technology,2010.
    [7]彭洪江,陈盛双,曾延安. 基于改进阈值函数的小波降噪算法与仿真分析[J]. 计算机应用与软件,2015 (10):188191. PENG H J,CHEN SH SH,ZENG Y AN. Wavelet denoising algorithm based on improved threshold function and its simulation analysis[J]. Computer Applications and Software, 2015(10):188191.
    [8]DI Z G,ZHANG J X,JIA C R. An improved wavelet threshold denosing algorithm[C].3rd International Conference on Intelligent System Design and Engineering Applications，2013:297299.
    [9]周克良,邢素林,聂丛楠. 基于自适应阈值小波变换的心音去噪方法[J]. 广西师范大学学报:自然科学版,2016,34(1):1925. ZHOU K L,XING S L, NIE C N. Heart sound denoising method based on adaptive threshold wavelet transform [J]. Journal of Guangxi Normal University:Natural Science Edition, 2016, 34(1):1925.
    [10]PAULET A S,WAN E A,NELSON A T. Noise reduction for heart sounds using a modified minimummean squared error estimator with ECG gating[C].28th Annual International Conference of the IEEE. Chicago: University of Chicago Press,2006:33853390.
    [11]林京. 基于最大似然估计的小波阈值消噪技术及信号特征提取[J]. 仪器仪表学报,2005,26(9):923927. LIN J. Wavelet denoising based on maximum likelihood estimation and its application for feature extraction[J]. Chinese Journal of Scientific Instrument, 2005, 26(9):923 927.
    [12]郭兴明,吴玉春,肖守中. 自适应提升小波变换在心音信号预处理中的应用[J]. 仪器仪表学报,2009,30(4):802806. GUO X M,WU Y CH,XIAO SH ZH. Application of adaptive lifting wavelet transform in preprocessing of heart sound signal[J]. Chinese Journal of Scientific Instrument, 2009, 30(4):802806.
    [13]郭兴明,丁晓蓉,钟丽莎,等. 小波包与混沌集成的心音特征提取及分类识别[J]. 仪器仪表学报,2012,33(9):19381944. GUO X M,DING X R,ZHONG L SH,et al. Heart sound feature extraction and classification based on integration of wavelet packet analysis and chaos theory[J]. Chinese Journal of Scientific Instrument, 2012, 33(9):19381944.
    [14]张伟,师奕兵,卢涛. 无线随钻泥浆信号小波包去噪处理[J]. 电子测量与仪器学报,2010,24(1):8084. ZHANG W,SHI Y B,LU T. Wavelet packet denoising method of wireless measurement while drilling[J]. Journal of Electronic Measurement and Instrument, 2010,24(1):8084.
    [15]LI J,CHENG C K,JIANG T Y. Wavelet denosing of partial discharge signals based on genetic adaptive threshold estimation[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2012,19(2):543549.
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余辉,姜博畅,刘雁飞,关红彦.基于小波收缩的心音降噪最优化分析[J].电子测量与仪器学报,2017,31(3):383-388

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