Empirical Mode Decomposition: Theory & Applications

Empirical Mode Decomposition (EMD), introduced by Huang et al, in 1998 is a new and effective tool to analyze non-linear and non-stationary signals. With this method, a complicated and multiscale signal can be adaptively decomposed into a sum of finite number of zero mean oscillating components called as Intrinsic Mode Functions (IMF) whose instantaneous frequency computed by the analytic signal method (process known as Hilbert Huang Transform) give a physically meaningful characterization of the signal. The EMD is based on the sequential extraction of energy associated with various intrinsic time scales of the signal starting from finer temporal scales (high frequency modes) to coarser ones (low frequency modes). This paper reviews the method of applying EMD to a signal and its various applications. Keywords— Empirical mode decomposition, Intrinsic mode function, Hilbert-Huang transform, Signal denoising, Adaptive, Biomedical signal analysis