Karagiannis Alexandros, Constantinou Philip
Mobile Radio Communications Laboratory, Electrical and Computer Engineering Department, National Technical University of Athens, Athens, Attiki GR-15773,
IEEE Trans Inf Technol Biomed. 2011 Jan;15(1):11-8. doi: 10.1109/TITB.2010.2091648. Epub 2010 Nov 11.
In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.
本文描述了一种方法，用于研究经验模态分解（EMD）在生物医学信号中的性能，特别是在心电图（ECG）的情况下。使用添加了白高斯噪声的合成ECG信号，并对不同长度的时间序列进行EMD处理，以提取固有模态函数（IMF）。实施统计显著性检验，以识别具有高噪声成分的IMF，并将其从去噪过程中排除。仿真结果表明，在生物医学时间序列应用EMD之前引入预处理阶段，可以减少处理时间。此外，还研究了根据预处理阶段类型，IMF数量随信噪比（SNR）和时间序列长度的变化情况。还介绍了该方法在麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）心电图记录中的应用，以验证在实际ECG信号中的研究结果。
