改进EEMD算法在心电信号去噪中的应用

集合经验模态分解（Ensemble empirical mode decomposition，EEMD）方法在去除心电信号噪声时，噪声本征模态函数（Intrinsic mode function，IMF）分量难以选择且将噪声分量直接去掉会导致信号失真。针对上述问题，提出了一种基于EEMD的自适应阈值算法。首先对含噪心电图（Electrocardiogram，ECG）数据进行EEMD分解，得到IMF，根据马氏距离进行信号IMF分量和噪声IMF分量的判定，然后通过果蝇优化算法确定噪声IMF的阈值，将经过阈值去噪的新的分量和剩余分量重构得到去噪后的ECG。最后，使用MIT-BIH数据库中的心电数据进行实验，实验结果表明，该方法在去噪同时能够较好地保留信号细节。

Application of Improved EEMD Algorithm in ECG Signal Denoising

In order to solve the problems that the intrinsic mode function(IMF) components are difficult to select and the noise components are always eliminated directly when removing the noise of the electrocardiogram(ECG) signal by using the ensemble empirical mode decomposition(EEMD) method, an adaptive thresholding algorithm based on EEMD is proposed. Firstly, the noisy ECG signal is decomposed to obtain the IMFs by the EEMD method, and then the noise IMFs and the siginal IMFs are judged according to the Mahalanobis distance. After that, the thresholding of the niose IMF is determined using the fruit fly optimization algorithm(FOA). The denoised ECG signals are reconstructed by the new IMFs and the rest of IMFs after thresholding denoising. Finally, the method is applied to ECG data in MIT-BIH database. The experimental results indicate that the method can preserve the signal details while denoising.