易于硬件实现滤除ECG信号运动干扰的变步长LMS算法
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摘要
针对移动心电(ECG)信号监测系统中运动干扰难以滤除的问题,提出了一种易于硬件实现的数字自适应变步长最小均方(LMS)算法。通过简化步长因子与输入信号的关系,减少了权值更新系统的运算量;分析传统LMS算法收敛性不稳定的问题,结合迭代次数优化步长因子,提高了算法的收敛性能。对比传统LMS算法,所提算法在运算量增加微小的情况下,收敛性能大幅提升,信噪比(SNR)增加大于14d B。仿真结果表明:算法在心电信号进行实时硬件集成滤除运动干扰方面具有运算量小,滤波效果好等优点。
In order to solve the problem that it is difficult to filter motion interference in electrocardio( ECG)signal monitoring system,an digital self-adaptive variable step-size least mean square( LMS) algorithm which is easy for hardware implementation is proposed. By simplifying the relationship between step-size factors and input signals,the algorithm reduces the computation of the weight updating system. Through analyzing unstablie convergence of traditional LMS algorithm,combined with iteration numbers to optimize the step-size factor,improve the convergence performance of the algorithm. Compared with the classical LMS algorithm,with computation slightly increased,convergence performance of the algorithm is greatly improved and the SNR is increased by more than 14 d B.Simulation results show that the algorithm has the advantages of small amount of computation and good to filter effect,while ECG sigal carry out realtime hardware integrated to filter motion interfernce.
In order to solve the problem that it is difficult to filter motion interference in electrocardio( ECG)signal monitoring system,an digital self-adaptive variable step-size least mean square( LMS) algorithm which is easy for hardware implementation is proposed. By simplifying the relationship between step-size factors and input signals,the algorithm reduces the computation of the weight updating system. Through analyzing unstablie convergence of traditional LMS algorithm,combined with iteration numbers to optimize the step-size factor,improve the convergence performance of the algorithm. Compared with the classical LMS algorithm,with computation slightly increased,convergence performance of the algorithm is greatly improved and the SNR is increased by more than 14 d B.Simulation results show that the algorithm has the advantages of small amount of computation and good to filter effect,while ECG sigal carry out realtime hardware integrated to filter motion interfernce.
引文
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[3]余冠成,赵晓东,俞乾,等.超低功耗智能移动心电监测设备[J].传感器与微系统,2015,34(3):74-76,80.
[4]牛群.可穿戴心电监测装置设计与实现[D].哈尔滨:哈尔滨工业大学,2016.
[5]杨少鹏,吴小培,张磊.基于ICA的胎儿心电提取系统[J].计算机系统应用,2011,20(9):22-26.
[6]王晓花,徐学军,何秋娅.一种ECG信号肌电干扰去除方法的研究[J].智能计算机与应用,2015,5(1):59-62.
[7]Hyejung K,Sunyoung K,Van Helleputte N,et al.Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system[C]∥Biomedical Circuits and Systems(Bio CAS)Conference,2012.
[8]Paulo S R Diniz.自适应滤波算法与实现[M].北京:电子工业出版社,2004.
[9]朱泽煌,胡广书,郭恒,等.MIT-BIH心电数据库的开发及用作检测标准[J].中国生物医学工程学报,1993(4):244-249.