经验模态分解(Empirical Mode Decomposition,简称EMD)法是美籍华人N. E. Huang等人于1998年提出的，适合于分析非线性、非平稳信号序列，具有很高的信噪比。该方法的关键是经验模式分解，它能使复杂信号分解为有限个本征模函数(Intrinsic Mode Function，简称IMF)，所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。(Empirical mode decomposition (EMD) is proposed by Chinese American N. E. Huang et al. In 1998. It is suitable for analyzing nonlinear and non-stationary signal sequences with high signal-to-noise ratio. The key of this method is empirical mode decomposition, which can make the complex signal is decomposed into a finite intrinsic mode functions (the Intrinsic Mode Function, referred to as IMF), the decomposition of the IMF component contains the local characteristics of signals with different time scales of the original signal.)