Quantile-Based Empirical Mode Decomposition: An Efficient Way to Decompose Noisy Signals

The main goal of this paper is to propose a new approach of empirical mode decomposition (EMD) that analyzes noisy signals efficiently. The EMD has been widely used to decompose nonlinear and nonstationary signals into some components according to intrinsic frequency called intrinsic mode functions. However, the conventional EMD may not be efficient in decomposing signals that are contaminated by noninformative noises or outliers. This paper presents a new EMD procedure that analyzes noisy signals effectively and is robust to outliers with holding the merits of the conventional EMD. The key ingredient of the proposed method is to apply a quantile smoothing method to a noisy signal itself instead of interpolating local extrema of the signal when constructing its mean envelope. Through simulation studies and texture image analysis, it is demonstrated that the proposed method produces substantially effective results.