Variable spectral segmentation empirical wavelet transform for noisy signal processing

Empirical wavelet transform (EWT) can successfully decompose a smooth or noise-free simulated signal into several components. This method has encountered difficulties in processing simulated signals containing noise and in applications. In order to improve the shortcomings of EWT and expand its application, this paper proposes a variable spectral segmentation EWT (VEWT) associated with the trend of spectral fluctuation. Different from scale-space representation and other optimized direct segmentation methods, this paper proposes a selective segmentation method. The extreme points of Multi-taper power spectral density (MPSD) are used to estimate the modes. An extended algorithm is proposed on the basis of Levenberg-Marquardt-Fletcher, which uses the extreme points and positions of MPSD to calculate the bandwidth corresponding to each mode. The information in each frequency band will be determined as the final mode. In the process of loop extraction, a set of boundaries associated with the fluctuations of the spectral will be obtained. The proposed method is more advantageous for the decomposition of signals containing noise. Simulated signals are used to verify the effectiveness of the proposed method. In addition, the MIT-BIH Arrhythmia Database is used to verify the applicability of the proposed method. (C) 2021 Elsevier Inc. All rights reserved.