Department of ECE, Delhi Technological University, Delhi, India.
Comput Methods Biomech Biomed Engin. 2021 Oct;24(13):1426-1436. doi: 10.1080/10255842.2021.1892659. Epub 2021 Mar 5.
With the aid of ensemble empirical mode decomposition (EEMD), de-noising of the electrocardiogram (ECG) signal based on the genetic particle filter and fuzzy thresholding is proposed in this paper, which effectively eliminates noise from the ECG signal. This paper proposes a two-phase scheme for removing noise from ECG signal. In the first phase, noisy signal is decomposed into true intrinsic mode functions (IMFs) with the help of EEMD. Adaptive EEMD (AEEMD) is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise are obtained by using spectral flatness of each IMF and fuzzy thresholding. Corrupted IMFs are filtered using genetic particle filter to remove the noise. Finally, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for different databases and it gives better signal-to-noise ratio and root mean square error than other existing techniques.
本文提出了一种基于遗传粒子滤波和模糊阈值的集合经验模态分解（EEMD）去噪方法，对心电图（ECG）信号进行去噪。该方法能有效去除心电图信号中的噪声。本文提出了一种两阶段方案，用于去除心电图信号中的噪声。在第一阶段，利用 EEMD 将噪声信号分解为真实的固有模态函数（IMF）。自适应 EEMD（AEEMD）优于 EMD，因为它消除了模态混合效应。在第二阶段，通过每个 IMF 的频谱平坦度和模糊阈值获得被噪声污染的 IMF。使用遗传粒子滤波器对受污染的 IMF 进行滤波以去除噪声。最后，用处理后的 IMF 重构信号，得到去噪后的 ECG。与其他现有技术相比，该算法在不同的数据库中进行了分析，具有更好的信噪比和均方根误差。
