|Table of Contents|

ECG Signal Denoising Algorithm based on Wavelet Packet Analysis and Singular Value Difference(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2022年04期
Page:
119-127
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
ECG Signal Denoising Algorithm based on Wavelet Packet Analysis and Singular Value Difference
Author(s):
Chen Siyu1; Zhang Beiwei1; Liu Xuemei2
(1.College of Information Engineering,Nanjing University of Finance & Economics,Nanjing 210023,China)
(2.Business School,Nanjing Normal University,Nanjing 210023,China)
Keywords:
wavelet packet decomposition; singular value decomposition; signal denoising; evaluation index
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2022.04.016
Abstract:
A wavelet packet analysis combined with singular value difference algorithm is proposed to denoise ECG signals in practical applications. The algorithm firstly performs wavelet packet decomposition on the noisy signal and divides the decomposed sub-bands into three groups using the cross-correlation coefficient:micro-correlation group,real-correlation group and explicit-correlation group; Next,perform singular value decomposition denoising on the real-correlation group and determine the singular value demarcation of the useful and noisy signals using the difference method; Finally,the real-correlation group after singular value decomposition is reconstructed with the explicit-correlation group to obtain the denoised signal. The experimental results show that the algorithm can effectively suppress noise in a variety of databases and under different noise levels. Compared with other methods reported in the literature,the signal-to-noise ratio of this method is higher,the root-mean-square error is smaller,and the denoised signal is more similar to the original signal and smoother,achieving a better denoising effect.

References:

[1]HU X Y,CHEN X X,REN R,et al. Adaptive filtering and characteristics extraction for impedance cardiography[J]. Journal of fiber bioengineering and informatics,2014,7(1):81-90.
[2]KRISHNAN,RAM S,SEELAMANTULA,et al. On the selection of optimum savitzky-golay filters[J]. IEEE transactions on signal processing,2013,61(2):380-391.
[3]张培玲,李小真,崔帅华. 基于改进小波阈值-CEEMDAN算法的ECG信号去噪研究[J]. 计算机工程与科学,2020,42(11):2067-2072.
[4]MOEIN S. An MLP neural network for ECG noise removal based on Kalman filter[J]. Advances in experimental medicine and biology,2010,680(680):109-116.
[5]何刘,丁建明,林建辉,等. 完全互补小波噪声辅助集总经验模态分解[J]. 振动与冲击,2017,36(4):232-242.
[6]张国华,袁中凡,李彬彬. 心音信号特征提取小波包算法研究[J]. 振动与冲击,2008(7):47-49,185.
[7]曹伟,孙红梅,贾瑞生,等. 基于小波包分解重构的微地震信号降噪方法[J]. 电子测量与仪器学报,2018,32(4):134-143.
[8]陈仁祥,汤宝平,吕中亮. 基于相关系数的EEMD转子振动信号降噪方法[J]. 振动. 测试与诊断,2012,32(4):542-546,685.
[9]HARMOUCHE J,FOURER D,AUGER F,et al. The sliding singular spectrum analysis:a data-driven nonstationary signal decomposition tool[J]. IEEE transactions on signal processing,2017,66(1):251-263.
[10]戴豪民,许爱强,孙伟超. 基于改进奇异谱分析的信号去噪方法[J]. 北京理工大学学报,2016,36(7):727-732,759.
[11]胡明宇,陈小桥,谢银波. 宽波段微型光谱仪的小波奇异值差分去噪[J]. 武汉大学学报(工学版),2021,54(3):269-276.
[12]张淼,魏国. 心电信号平滑分解阈值去噪方法[J]. 哈尔滨工程大学学报,2020,41(9):1329-1339.
[13]童力,刘翰柔,胡松涛,等. 听觉诱发电位信号分析中小波基函数的确定[J]. 科学技术与工程,2021,21(2):473-479.

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Last Update: 2022-12-15