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Types of EMD Algorithms

In this paper, we present an algorithm based on the Empirical Mode Decomposition (EMD) method and compare it with two other existing EMD algorithms. EMD is a data-driven method that used as a propelling tool for analyzing and decomposing non-stationary and non-linear data without any previous assumption like choosing mother function in Wavelet. EMD generates a finite and often small number of Intrinsic Mode Functions (IMF). The decomposition is based on the direct extraction of the energy-related to various intrinsic time scales, an important parameter of the system. We first introduce the EMD method and its limitations, two existing EMD algorithms, and compare the two algorithms. Then, an improved EMD algorithm is presented. The three algorithms are tested on several time functions with different frequency characteristics. Results show the proposed algorithm provides no spurious IMF for these functions and is not placed in an infinite loop. Finally, the three algorithms are applied to analyze an ECG signal. Results reveal the presented algorithm is an appropriate algorithm to be used for analyzing biological signals.