Adaptive denoising algorithm using peak statistics-based thresholding and novel adaptive complementary ensemble empirical mode decomposition.

• NACEEMD method is proposed to suppress the mode-mixing problem of EEMD. • A new screening criterion is proposed to screen out the effective IMFs. • EPAD algorithm is developed to implement the peak statistics-based thresholding. • Explored and expanded the application potential of the proposed denoising algorithm. This paper proposes an adaptive denoising methodology for noisy signals that employs a novel adaptive complementary ensemble empirical mode decomposition (NACEEMD) and a peak statistics (PS)-based thresholding technique. The key idea in this paper is the peak statistics (PS)-based thresholding technique,which breaks the traditional strategy with respect to selecting more accurate and more adaptive thresholds. The NACEEMD algorithm is proposed to decompose the noisy signal into a series of intrinsic mode functions (IMFs). At the same time, NACEEMD is also used to verify the applicability of the PS-based thresholding technique in different decomposition algorithms. The PS-based threshold is used to remove the noise inherent in noise-dominant IMFs, and the denoised signal is reconstructed by combining the denoised noise-dominant IMFs and the signal-dominant IMFs. This paper uses a various of simulated signals in various noisy environments for experiments, the experimental results indicate that the proposed algorithm outperforms traditional threshold denoising methodologies in terms of signal-to-noise ratio, root mean square error, and percent root distortion. Moreover, through real ECG signal and multi-sensor data fusion experiments, the application of the proposed algorithm in the field of engineering is explored and expanded. [ABSTRACT FROM AUTHOR]