【摘要】 实测心电(ECG)信号通常被多种因素干扰,尤其是肌电干扰的去除存在较大困难。本文提出一种结合经验模态分解法(EMD)与主成分分析(PCA)的消噪算法来去除ECG信号的肌电干扰。解决了通常采用小波算法和EMD等方法会导致ECG信号产生振荡和丢失有用信息的难题。本研究利用PCA对含噪信号经EMD分解后的内蕴模态函数(IMF)进行去噪处理,通过对MIT-BIH心电数据进行仿真,以及定性分析了信噪比(SNR)和均方误差(MSE)。结果表明,ECG信号中的肌电干扰被有效去除,所提方法的消噪效果整体上优于小波去噪算法和EMD消噪算法,是一种有效的消噪方法。更多还原

【Abstract】 The Electrocardiogram( ECG) recording is often deteriorated by several factors,especially the myoelectricity interference removal is more difficult.In this paper,removing the electromygram from ECG signal based on empirical mode decomposition( EMD) and principal component analysis( PCA) de-nosing method has been proposed.The problem of shocking of ECG signal、lose some useful information by wavelet algorithm and EMD respectively has been solved.The method removed noise of intrinsic mode functions( IMFs) using PCA,after the noisy signal is decomposed by EMD.At last,the proposed method is evaluated over MIT-BIH ECG database in terms of visual inspection and qualitatively by signal noise ratio( SNR) and mean square error( MSE).The results show that the myoelectricity interference are removed efficiently and the proposed method outperforms wavelet de-noising algorithm and EMD de-noising algorithm.So it is an effective de-noising method.更多还原