School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, China.
PLoS One. 2020 Jul 15;15(7):e0235330. doi: 10.1371/journal.pone.0235330. eCollection 2020.
Electrocardiogram (ECG) denoising is a biomedical research area of great importance. In this paper, an integrated empirical mode decomposition adaptive threshold denoising method (IEMD-ATD) is proposed for processing ECGs. Three methods are included in the IEMD-ATD. First, an integrated EMD method based on a framework of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed to improve the decomposition quality and stability of raw ECGs. Second, a new grouping method for intrinsic mode functions (IMFs) is developed based on the energy and eigenperiod of IMFs. The grouping method is able to determine the boundaries among high-frequency noise predominant IMFs, useful information predominant IMFs and IMFs with low-frequency noises. Finally, an adaptive threshold denoising method is derived and used for denoising high-frequency noise predominant IMFs. There are two main contributions: 1) an adaptive threshold determination method based on the 3σ criterion and 2) a peak filtering denoising method for retaining useful information contained in the values smaller than the threshold. Synthetic and real ECG data in the MIT-BIH database are utilised in experiments to illustrate the effectiveness of IEMD-ATD for ECG denoising. The results indicate that IEMD-ATD offers better performance in improving the signal-to-noise ratio (SNR) and correlation coefficient compared with the existing EMD denoising methods. Our method offers obvious advantages, especially in retaining detailed information on the QRS complex of the ECG, which is significant for the feature extraction of ECG signals and for pathological diagnosis.
心电图（ECG）去噪是生物医学研究中一个非常重要的领域。本文提出了一种基于完备集合经验模态分解自适应噪声的集成经验模态分解自适应阈值去噪方法（IEMD-ATD），用于处理心电图。IEMD-ATD 包含三种方法。首先，提出了一种基于完全集合经验模态分解自适应噪声的集成 EMD 方法（CEEMDAN），以提高原始 ECG 的分解质量和稳定性。其次，根据 IMF 的能量和本征周期，提出了一种新的 IMF 分组方法。该分组方法能够确定高频噪声主导 IMF、有用信息主导 IMF 和低频噪声 IMF 之间的边界。最后，推导并使用自适应阈值去噪方法对高频噪声主导 IMF 进行去噪。主要有两个贡献：1）基于 3σ 准则的自适应阈值确定方法，2）用于保留阈值以下值中包含的有用信息的峰值滤波去噪方法。实验中使用了麻省理工学院-贝斯以色列女执事医疗中心（MIT-BIH）数据库中的合成和真实心电图数据来验证 IEMD-ATD 对心电图去噪的有效性。结果表明，与现有的 EMD 去噪方法相比，IEMD-ATD 在提高信噪比（SNR）和相关系数方面具有更好的性能。与其他方法相比，我们的方法具有明显的优势，特别是在保留心电图 QRS 波群的详细信息方面，这对于心电图信号的特征提取和病理诊断具有重要意义。
