基于小波提升的ECG去噪和QRS波识别快速算法  

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

作　　者：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] 

机构地区：[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

出　　处：《吉林大学学报（工学版）》2012年第4期1037-1043,共7页Journal of Jilin University:Engineering and Technology Edition

基　　金：广东省教育厅产学研结合项目(2009B090300260);珠海市科技计划项目(2010B020102021)

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。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.

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 

分 类 号：TN911.72[电子电信—通信与信息系统]