[1]王晓燕,鲁华祥,金敏,等.基于相关性的小波熵心电信号去噪算法[J].智能系统学报,2016,11(6):827-834.[doi:10.11992/tis.201611017]
WANG Xiaoyan,LU Huaxiang,JIN Min,et al.Wavelet entropy denoising algorithm of electrocardiogram signals based on correlation[J].CAAI Transactions on Intelligent Systems,2016,11(6):827-834.[doi:10.11992/tis.201611017]
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WANG Xiaoyan,LU Huaxiang,JIN Min,et al.Wavelet entropy denoising algorithm of electrocardiogram signals based on correlation[J].CAAI Transactions on Intelligent Systems,2016,11(6):827-834.[doi:10.11992/tis.201611017]
基于相关性的小波熵心电信号去噪算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第6期
页码:
827-834
栏目:
学术论文—机器学习
出版日期:
2017-01-20
- Title:
- Wavelet entropy denoising algorithm of electrocardiogram signals based on correlation
- Keywords:
- electrocardiogram signals; denoising; correlation; wavelet entropy; adaptively
- 分类号:
- TP391
- 摘要:
- 针对心电信号的基线漂移、工频噪声、肌电噪声，本文提出了基于相关性的小波熵去噪算法。算法首先根据基线漂移的低频特性，确定小波分解的层数，置零近似系数，去除基线漂移；再对相邻尺度的高频小波系数进行相关处理，依据小波熵自适应地计算全局阈值去除工频和肌电噪声；最后将置零的近似系数和阈值处理后的小波系数重构得到有效信号。该算法能够在一次小波分解、重构的过程中，同时滤除心电信号中的3种主要噪声。对MIT-BIH数据库数据和模拟数据的仿真实验结果也表明该算法的去噪效果显著优于其他算法。
- Abstract:
- In view of the baseline drift, power line interference and muscle noise of electrocardiogram (ECG) signals, the wavelet entropy denoising algorithm of ECG signals based on correlation was proposed. First, ECG signals were decomposed using wavelets to determine the number of scale of wavelet decomposition, and the lowest approximation coefficients were each set to zero, so as to remove the baseline drift. Then, the high-frequency wavelet coefficient of adjacent scales was processed by adaptively calculating the global threshold with the correlation coefficients between the adjacent scales, to remove the power line interference and the muscle noise. Last, the denoising signals were reconstructed using zero approximation coefficients and processed wavelet coefficients. Using this method, three kinds of noise were removed in one process of wavelet decomposition and reconstruction. Experiments using the MIT-BIH database and simulative data prove that the algorithm is much better than others in ECG denoising with low complexity.
- 参考文献/References:
-
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备注/Memo
收稿日期:2016-11-15。
基金项目:中国科学院战略性先导专项（xdb02080002）；青年自然科学基金项目（61401423）；中国科学院国防实验室基金项目（CXJJ-16S076）.
作者简介:王晓燕,女,1992年,硕士研究生,主要研究方向为信号处理、机器学习;鲁华祥,男,1965年,研究员,博士生导师,中国人工智能学会"神经网络与计算智能"专业委员会主任、中科院半导体所高速电路与神经网络实验室主任,主要研究方向为类脑神经计算方法、微电子类神经计算芯片和系统研究、不确定性及非完全信息处理。获北京市科学技术进步一等奖、"中国科学院盈科优秀青年学者奖"、国家发明三等奖、"国家‘八五’科技攻关重大科技成果奖"、"95电子十大科技成果奖"、全军科技进步二等奖等奖励。发表学术论文30余篇,合作出版专著1本,获授权发明专利10项;金敏,女,1985年,助理研究员,主要研究方向为信号处理、智能计算。
通讯作者:鲁华祥.E-mail:luhx@semi.ac.cn.
更新日期/Last Update:
1900-01-01