[1]陈思雨,张备伟,刘雪梅.小波包分析联合奇异值差分的心电信号去噪算法[J].南京师大学报(自然科学版),2022,45(04):119-127.[doi:10.3969/j.issn.1001-4616.2022.04.016]
　Chen Siyu,Zhang Beiwei,Liu Xuemei.ECG Signal Denoising Algorithm based on Wavelet Packet Analysis and Singular Value Difference[J].Journal of Nanjing Normal University(Natural Science Edition),2022,45(04):119-127.[doi:10.3969/j.issn.1001-4616.2022.04.016]
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小波包分析联合奇异值差分的心电信号去噪算法()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第45卷
期数:
2022年04期
页码:
119-127
栏目:
计算机科学与技术
出版日期:
2022-12-15

文章信息/Info

Title:
ECG Signal Denoising Algorithm based on Wavelet Packet Analysis and Singular Value Difference
文章编号:
1001-4616(2022)04-0119-09
作者:
陈思雨1; 张备伟1; 刘雪梅2
(1.南京财经大学信息工程学院,江苏 南京 210023)
(2.南京师范大学商学院,江苏 南京 210023)
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)
关键词:
小波包分解; 奇异值分解; 信号去噪; 评估指标
Keywords:
wavelet packet decomposition; singular value decomposition; signal denoising; evaluation index
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2022.04.016
文献标志码:
A
摘要:
针对心电信号在实际应用中存在大量噪声的问题,提出一种小波包分析联合奇异值差分的心电信号去噪算法. 该算法首先对含噪信号进行小波包分解,利用互相关系数将分解得到的子频带分为3组:微相关组、实相关组和显相关组; 其次对实相关组执行奇异值分解去噪,并利用差分法确定有用信号与噪声信号的奇异值分界; 最后将显相关组与奇异值分解后的实相关组进行重构,得到去噪后的信号. 实验结果表明,在多种数据库以及不同噪声水平下,该算法均可有效抑制噪声,与文献中报道的其他方法相比,本文方法信噪比更高,均方根误差更小,去噪后的信号与原始信号相似度更高,且更为平滑,取得较好的去噪效果.
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2022-06-10.
基金项目:国家自然科学基金资助项目(61973152、60802087)、江苏省研究生科研与实践创新计划基金项目(CSYXW21002).
通讯作者:张备伟,博士,副教授,研究方向:大数据处理、图像处理、模式识别. E-mail:zhangbeiwei@nufe.edu.cn
更新日期/Last Update: 2022-12-15