›› 2012, Vol. 42 ›› Issue (04): 1037-1043.

• 论文 • 上一篇    下一篇

基于小波提升的ECG去噪和QRS波识别快速算法

姚成1,2, 司玉娟1,3, 郎六琪1,3, 朴德慧2, 徐海峰2, 李贺佳2   

  1. 1. 吉林大学 通信工程学院, 长春 130022;
    2. 装甲兵技术学院, 长春 130117;
    3. 吉林大学 珠海学院, 广东 珠海 519041
  • 收稿日期:2011-07-10 出版日期:2012-07-01 发布日期:2012-07-01
  • 通讯作者: 司玉娟(1963-),女,教授,博士生导师.研究方向:通信与信息系统研究.E-mail:yujuansi@163.com E-mail:yujuansi@163.com
  • 基金资助:
    广东省教育厅产学研结合项目(2009B090300260);珠海市科技计划项目(2010B020102021).

Fast algorithm of ECG denoising and QRS wave identification based on wavelet lifting

YAO Cheng1,2, SI Yu-juan1,3, LANG Liu-qi1,3, PIAO De-hui2, XU Hai-feng2, LI He-jia2   

  1. 1. College of Communication Engineering, Jilin University, Changchun 130022, China;
    2. The Institute of Changchun Engineering Technology, Changchun 130117, China;
    3. College of Zhuhai, Jilin University, Zhuhai 519041, China
  • Received:2011-07-10 Online:2012-07-01 Published:2012-07-01

摘要: 提出了一种基于小波提升的ECG去噪和QRS波识别的快速算法。该算法在小波提升基础上引入加权阈值收缩法,保证ECG有用信息不丢失,提高了去噪效果;利用去噪重构的中间结果并结合简单的差分法,实现了使用平滑函数一阶导数对信号进行小波提升变换,避免了需要二次小波提升变换的运算,在保证识别精度的同时,大大降低了运算复杂度。实验结果表明,该算法能得到较高的SNR和较低的MSE,QRS波识别准确率达到了99.5%以上。并且,该算法利于在硬件平台(FPGA)上实现,便于在心电监护设备上集成。

关键词: 信息处理技术, ECG去噪, 提升小波, 加权阈值收缩, QRS波识别, 差分

Abstract: This paper proposes a fast algorithm of ECG denoising and QRS wave identification based on wavelet lifting. On the basis of wavelet lifting, the weighted threshold shrinkage method is introduced to ensure not to lose useful ECG information and improve the denoising effect. Using the intermediate result of the denoising and reconstruction together with the simple finite difference method, the proposed algorithm uses the first derivative of the smooth function to process the signals by the lifting wavelet transform. Such process can avoid the secondary operation of the lifting wavelet transform, thus significantly reducing the complexity of operation meanwhile maintaining the identification precision. Experimental results demonstrate that the proposed algorithm can achieve relatively higher SNR and lower MSE. In addition, the accuracy rate of QRS wave identification is above 99.5%. Moreover, this algorithm can be realized on the hardware platform of FPGA, which is convenient for ECG monitoring equipment integration.

Key words: information processing technology, ECG de-noising, lifting wavelet, weighted threshold shrinkage, QRS wave identification, finite difference

中图分类号: 

  • TN911.72
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