噪声环境下起搏心电信号的压缩感知重构算法  

Compressed sensing recovery algorithm of pacing ECG in noise environment

作　　者：朱凌云[1] 李汶松[1] 向南[1] ZHU Lingyun;LI Wensong;XIANG Nan(College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China)

机构地区：[1]重庆理工大学计算机科学与工程学院,重庆400054

出　　处：《计算机工程与应用》2017年第18期61-66,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61502064);重庆市自然科学基金(No.cstc2011jj A40002);重庆市教委科学技术研究项目(A类)(No.KJ110813)

摘　　要：针对传统压缩感知重构算法在起搏心电信号远程监测过程中易受噪声干扰的问题,提出在利用正交匹配追踪进行残差更新的迭代过程中引入岭回归正则化参数K,降低噪声对重构结果的影响。利用岭迹法证明了最佳K值与信噪比呈负相关,为选取K值以获得更接近真实解的重构信号提供了理论依据。对基于岭回归的重构算法与分块稀疏贝叶斯学习算法、正交匹配追踪算法进行了对比分析,实验结果表明,在低信噪比环境下,引入了岭回归思想的算法在保留高重构效率的同时提高了重构精度。Conventional reconstruction methodologies of compressed sensing for pacing ECG is suffered significantlyfrom various noise through telemonitoring pacemaker by wireless communication networks.A novel method with theridge regression regularization parameter K in the iteration of residual error is proposed to reduce the impact of noise tothe recovery results.By ridge regression,it proves that superior K is negatively related to SNR,which provides a theoreticalevidence to choose the suitable K and obtains the most accurate recovery signal.Comparative analysis is also introducedamong the recovery algorithm based on the ridge regression,the Block Sparse Bayesian Learning and the orthogonalmatching pursuit.The results show that the ridge regression method can not only maintain high recovery efficiency underthe low SNR environment and increase the recovery accuracy of pacing ECG simultaneously.

关 键 词：压缩感知 起搏心电信号 重构算法 岭回归 

分 类 号：TN911[电子电信—通信与信息系统]