基于遗传优化函数曲线的小波阈值法心电信号除噪

doi:10.11772/j.issn.1001-9081.2014.09.2600

计算机应用 ›› 2014, Vol. 34 ›› Issue (9): 2600-2603.DOI: 10.11772/j.issn.1001-9081.2014.09.2600

基于遗传优化函数曲线的小波阈值法心电信号除噪

王争,何宏,谭永红

上海师范大学信息与机电工程学院，上海 200234

收稿日期:2014-03-21 修回日期:2014-06-03 出版日期:2014-09-01 发布日期:2014-09-30
通讯作者: 何宏
作者简介:
王争(1990-)，男，江苏连云港人，硕士研究生，主要研究方向：模式识别、医学信息处理；
何宏(1973-)，女，四川射洪人，副教授，博士，主要研究方向：模式识别、生物医学信息处理；
谭永红(1958-)，男，广西桂林人，教授，博士，主要研究方向：系统建模、智能控制、生物医学信号处理。
基金资助:
国家自然科学基金资助项目;上海市教委科研创新项目;上海市自然科学基金资助项目

Wavelet thresholding method based on genetic optimization function curve for ECG noise removal

WANG Zheng,HE Hong,TAN Yonghong

College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China

Received:2014-03-21 Revised:2014-06-03 Online:2014-09-01 Published:2014-09-30
Contact: HE Hong
Supported by:
Signal analysis and nonlinear compensation of IPMC sensors;Filtering and state estimation of non-smooth stochastic sandwich systems with hysteresis;Patten recognition of nonlinear meridian information system with multiple variable coupling

摘要/Abstract

摘要：

针对小波阈值滤波方法中硬阈值方法易产生震荡和软阈值方法易产生波形失真的缺点，提出了一种基于遗传优化函数曲线的小波阈值法GOCWT。该方法利用二次函数模拟阈值转换函数曲线，并根据均方根误差(RMSE)与平滑度建立适应度函数，运用遗传算法(GA)对转换函数参数进行寻优。通过对48段心电信号滤波性能指标分析发现：与硬阈值滤波方法相比，GOCWT的平滑度性能提升了36%；与软阈值滤波方法相比，其均方根误差性能提升了32%。实验结果表明，GOCWT的滤波性能优于硬、软阈值滤波方法，既避免了心电信号滤波时产生的震荡现象，同时又很好地保留了信号的峰值等细节特征。

Abstract:

In order to overcome the oscillation caused by hard threshold wavelet filtering and the waveform distortion brought by soft threshold wavelet filtering, a wavelet threshold de-noising method based on genetic optimization function curve named GOCWT was proposed. In the GOCWT, a quadratic function was used to simulate the optimal threshold function curve. The Root Mean Square Error (RMSE) and smoothness of the reconstructed signal were applied to design the fitness function. Furthermore, the Genetic Algorithm (GA) was utilized to optimize the parameters of the new thresholding function. Through the analysis of 48 segments of ECG signals, it was found that the new method resulted in a 36% increase of smoothness value comparing to the hard threshold method, and a 32% decrease of RMSE value comparing to the soft threshold method. The results show that the proposed algorithm outperforms hard threshold wavelet filtering and soft threshold wavelet filtering, it can not only avoid the undesirable oscillation phenomenon of the filtered signal, but also reserve the minute features of the signal including peak value.

中图分类号:

TP18

王争何宏谭永红. 基于遗传优化函数曲线的小波阈值法心电信号除噪[J]. 计算机应用, 2014, 34(9): 2600-2603.

WANG Zheng HE Hong TAN Yonghong. Wavelet thresholding method based on genetic optimization function curve for ECG noise removal[J]. Journal of Computer Applications, 2014, 34(9): 2600-2603.

参考文献

［1］〖BP(〗ZHONG Y, XU L, YAN L, et al.Adaptive R-wave detection method in dynamic ECG with heavy EMG artifact ［C］// ICIA 2012: Proceedings of the 2012 International Conference on Information and Automation. Piscataway: IEEE, 2012: 83-87.〖BP)〗

YUE Z. Adaptive R-wave detection method in dynamic ECG with heavy EMG artifact [C]// ICIA 2012: Proceedings of the 2012 International Conference on Information and Automation. Piscataway: IEEE, 2012: 886-893.

［2］DEBBABI N, EI ASMI S. Algebraic approach for R-peak detection in the ElectroCardioGram (ECG) signal［C］// ICSCS 2012: Proceedings of the 2012 1st International Conference on System and Computer Science. Piscataway: IEEE, 2012: 1-5.

［3］SONALI O, SINGH O, SUNKARIA R K. ECG signal denoising based on empirical mode decomposition and moving average filter［C］// ISPCC 2013: Proceedings of the 2013 IEEE International Conference on Signal Processing, Computing and Control. Piscataway: IEEE, 2013: 677-690.

［4］RAO N, LI L.Biomedical signal processing［M］. Chengdu: University of Electronic Science and Technology of China Press, 2005. (饶妮妮,李凌.生物医学信号处理［M］.成都:电子科技大学出版社,2005.)

［5］DONOHO D L. De-nosing by soft-thresholding［J］. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.

［6］DONOHO D L, JOHNSTONE I M. Ideal spatial adaption via wavelet shrinkage［J］. Biometrika, 1994, 81(3): 425-455.

［7］DONOHO D L, JOHNSTONE I M. Adapting to unknown smoothness via wavelet shrinkage［J］. Journal of the American Statistical Association, 1995, 90(432): 1200-1224.

［8］DONOHO D L, JOHNSTONE I M, KERKYACHARIAN G. Wavelet shrinkage :Asymptopia［J］. Journal of the Royal Statistical Society: Series B (Methodological), 1995, 57(2): 301-369.

［9］YIN X, LIU A, LIU Y. Study if improved modulus maximum algorithm based on thresholding wavelet in ultrasonic testing signal de-noising for super alloy［C］// ICMTMA 2010: Proceedings of the 2010 Inernatona Conference on Measuring Technology and Mechatronics Automtion. Pisctaway: IEEE, 2010: 552-555.

［10］DONG X, YUE Y, QIN X, et al.Signal denoising based on improved wavelet packet thresholding function ［C］// CMCE 2010: Proceedings of the 2010 Internal Conference on Mechatronics, Control and Electronic Engineering. Piscataway: IEEE, 2010, 6: 382-385.

［11］LI L, MA L, DUAN Y. An improved wavelet threshold denoising algorithm and its sumulation analysis［J］. Process Automation Instrumentation, 2011, 32(7): 21-24. (李永军,马立元,段永刚.一种改进的小波阈值去噪算法及其仿真分析［J］.自动化仪表,2011,32(7):21-24.)

〖HJ1.45mm〗［12］TIAN C, LI B. An improved SAR image speckle reduction algorithm of wavelet threshold［C］// RSETE 2012: Proceedings of the 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering. Piscataway: IEEE, 2012: 1-4.

［13］YANG Q, CHAO X, LIU Y. The research of fault detection and diagnosis based on lifting scheme wavelet and threshold denoising［J］. Transactions of Shenyang Ligong University, 2012,31(6):55-60.(杨青,晁晓洁,刘云琦.提升小波阈值去噪的故障检测与诊断方法研究［J］.沈阳理工大学学报,2012,31(6):55-60.)

［14］LI S, JI Y, LIU G. Optimal wavelet basis selection of wavelet shrinkage for ECG de-noising ［C］// MASS '09: Proceedings of the 2009 International Conference on Management and Service Science. Piscataway: IEEE, 2009: 1-4.

［15］MOODY G B, MARK R G. The impact of the MIT-BIH arrhythmia database [J]. IEEE Engineering in Medicine and Biology Magazine, 2001, 20(3): 45-50.

〖BP(〗http://ecg.mit.edu/george/publications/mitdb-embs-2001.pdf〖BP)〗

［16］LIU X. Meridian points ECG signal feature extractio and pattern mining［D］. Shanghai: Shanghai Normal University, 2013. (刘鑫.经络穴位心电信号的特征提取与模式挖掘［D］.上海师范大学,2013.)

[1]	杜航原郝思聪王文剑. 结合图自编码器与聚类的半监督表示学习方法[J]. 计算机应用, 0, (): 0-0.
[2]	陈露张晓霞于洪. 基于先验知识的非负矩阵半可解释三因子分解算法[J]. 计算机应用, 0, (): 0-0.
[3]	韩舒宁徐敏董学士林青沈凡凡. 混合伊藤算法求解多尺度着色旅行商问题[J]. 计算机应用, 0, (): 0-0.
[4]	李晓杰崔超然宋广乐苏雅茜吴天泽张春云. 基于时序超图卷积神经网络的股票趋势预测方法[J]. 计算机应用, 0, (): 0-0.
[5]	张建严珂马祥. 基于神经网络的复杂垃圾信息过滤算法分析[J]. 计算机应用, 0, (): 0-0.
[6]	邱云志汪廷华戴小路. 双重特征加权模糊支持向量机[J]. 计算机应用, 0, (): 0-0.
[7]	李宗正周恺卿丁雷欧云. 基于基因交换的自适应人工鱼群算法[J]. 计算机应用, 0, (): 0-0.
[8]	刘清华廖士中. 基于随机素描方法的在线核回归[J]. 计算机应用, 0, (): 0-0.
[9]	张小清王晨曦吕彦林耀进. 基于ReliefF的层次分类在线流特征选择算法[J]. 计算机应用, 0, (): 0-0.
[10]	于婉莹梁美玉王笑笑陈徵曹晓雯. 基于深度注意力网络的课堂教学视频中学生表情识别与智能教学评估[J]. 计算机应用, 0, (): 0-0.
[11]	黄勇康梁美玉王笑笑陈徵曹晓雯. 基于深度时空残差卷积神经网络的课堂教学视频中多人课堂行为识别[J]. 计算机应用, 0, (): 0-0.
[12]	康猛蒙祖强. 基于局部条件区分能力的高效属性约简算法[J]. 计算机应用, 0, (): 0-0.
[13]	谢鑫张贤勇王旋晔唐鹏飞. 变精度邻域等价粒邻域决策树构造算法[J]. 计算机应用, 0, (): 0-0.
[14]	刘忠慧王梓宥闵帆. 近似概念的遗传生成算法及其推荐应用[J]. 计算机应用, 0, (): 0-0.
[15]	潘仁志钱付兰赵姝张燕平. 基于卷积神经网络交互的用户属性偏好建模的推荐模型[J]. 计算机应用, 0, (): 0-0.

基于遗传优化函数曲线的小波阈值法心电信号除噪

Wavelet thresholding method based on genetic optimization function curve for ECG noise removal

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics