Intrinsic Pattern Preserving Boundary Treatment Method for Empirical Mode Decomposition

Abstract

Empirical mode decomposition (EMD) is a procedure that decomposes a signal into so-called intrinsic mode functions (IMFs) according to the levels of local frequency. Due to its robustness to nonlinear and nonstationary signals, EMD has been widely used in various fields. However, EMD suffers from boundary problems severely. In this paper, an efficient method for boundary treatment is proposed. The proposed method consists of two stages. In the first stage, regression models are adapted to reproduce the intrinsic sinusoid pattern of a signal. Based on predicted values, the signal is extended beyond the boundaries in the second stage. Results from numerical studies including simulation study and a noisy signal analysis demonstrate that the proposed method alleviates the boundary problem and hence provides more accurate decomposition results.