基于改进EEMD的心电信号基线漂移消除方法

针对传统方法滤波效果不佳的问题，本文提出了基于改进集合经验模态分解（Ensemble empirical mode decomposition，EEMD）的消除心电信号基线漂移方法。该方法克服了经验模态分解（Empirical mode decomposition，EMD）模态混叠的问题，并对EEMD方法存在的问题和不足进行改进，建立集合经验模态分解方法中加入辅助白噪声大小的可依据准则，从而确定加入的辅助白噪声大小以及集合平均次数这两个重要参数。它从含噪心电信号中提取基线漂移信号，然后重构其余本征模函数（Intrinsic mode function，IMF）分量得到"干净"的心电信号，为后续的研究提供前提。经实验验证表明：相较于传统方法，这种方法能够提高信噪比、降低均方差、保持特征波形、去噪更加彻底，很好地解决了心电信号低频成分损失的问题。

A Method for Removing Baseline Drift in ECG Signal Based on Improved EEMD

A method to eliminate baseline drift of ECG signal based on improved ensemble empirical mode decomposition is proposed for the disadvantage of poor filtering in traditional method. The method can weaken the mode mixing of empirical mode decomposition, and make up for the shortcomings of EEMD. It establishes the criterion for adding auxiliary white noise in EEMD method, and then determines the two important parameters,i.e., the magnitude of auxiliary white noise and the ensemble times. The method extracts the baseline drift signal from the noisy signal and then reconstructs intrinsic mode function to obtain the "clean" ECG signal, which provides a prerequisite for subsequent research. The experimental results show that the de-noising method, compared with the traditional method, can improve the SNR, reduce root mean square error, keep the characteristic of the waveform, and solve the problem of low frequency component loss.