Fleureau Julien, Nunes Jean-Claude, Kachenoura Amar, Albera Laurent, Senhadji Lotfi
LTSI, Laboratoire Traitement du Signal et de l'Image INSERM : U642 Université de Rennes I FR.
IEEE Trans Signal Process. 2011 Mar;59(3):1309-1316. doi: 10.1109/TSP.2010.2097254.
A novel Empirical Mode Decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characteristic points, which offers the possibility to decompose multivariate signals without any projection. The scope of application of the novel algorithm is specified, and a comparison of the 2T-EMD technique with classical methods is performed on various simulated mono- and multivariate signals. The monovariate behaviour of the proposed method on noisy signals is then validated by decomposing a fractional Gaussian noise and an application to real life EEG data is finally presented.
本文提出了一种名为2T-EMD的新型经验模态分解（EMD）算法，用于单变量和多变量信号。它与其他方法的不同之处在于其计算轻便性和算法简单性。该方法主要基于对信号平均包络的重新定义，通过新的特征点计算得出，这使得无需任何投影即可分解多变量信号成为可能。明确了该新型算法的应用范围，并在各种模拟的单变量和多变量信号上对2T-EMD技术与经典方法进行了比较。然后，通过分解分数高斯噪声验证了该方法在噪声信号上的单变量行为，最后展示了其在实际脑电图数据中的应用。
