Empirical Mode Decomposition (EMD), an adaptive technique for data and signal decomposition, is a valuable tool for many applications in data and signal processing. One approach to EMD is the iterative filtering EMD, which iterates certain banded Toeplitz operators in l∞(Z). The convergence of iterative filtering is a challenging mathematical problem. In this paper we study this problem, namely...

An automatic sleep staging method is proposed to score wakefulness and three nonrapid eye movement (NREM) stages S1, S2 and S3, based on the Empirical Mode Decomposition (EMD) algorithm. Patients with sleep disorders were tested using this method. Good agreements between manual and automatic staging results were achieved in terms of their Cohen's Kappa value.

Á trous wavelet transform (AWT) and empirical mode decomposition (EMD) are two distinct methods used for analyzing nonlinear and nonstationary signals. In this paper, a combination of AWT and EMD is proposed as an improvedmethod for fusing remote sensing images on the basis of the framework of AWT-based image fusion. The principle consists of performing a multiresolution decomposition on high r...

A hybrid denoising method is presented as a combination of Empirical Mode Decomposition (EMD) and Higher Order Statistics (HOS). EMD, an adaptive data-driven method, is used for effective decomposition of a noisy signal into its functional components. Then Kurtosis and Bispectrum operate as Gaussianity estimators, supplemented by Bootstrap techniques, ensuring detection and removal of the signa...

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...

In this study, two new approaches for speech signal noise reduction based on the empirical mode decomposition (EMD) recently introduced by Huang et al. (1998) are proposed. Based on the EMD, both reduction schemes are fully data-driven approaches. Noisy signal is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs), using a temporal decomposition called sifti...

In this paper, we describe a novel technique for image fusion and enhancement, using Empirical Mode Decomposition (EMD). EMD is a non-parametric data-driven analysis tool that decomposes non-linear non-stationary signals into Intrinsic Mode Functions (IMFs). In this method, we decompose images, rather than signals, from different imaging modalities into their IMFs. Fusion is performed at the de...

The Hilbert-Huang transform (HHT) is a powerful method for nonlinear and non-stationary vibrations analysis. This approach consists of two basic parts of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). To achieve the reliable results, Bedrosian and Nuttall theorems should be satisfied. Otherwise, the phase and amplitude functions are mixed together and consequently, the ...

Following the “decomposition-and-ensemble” principle, the empirical mode decomposition (EMD)-based modeling framework has been widely used as a promising alternative for nonlinear and nonstationary time series modeling and prediction. The end effect, which occurs during the sifting process of EMD and is apt to distort the decomposed sub-series and hurt the modeling process followed, however, ha...

Reverberation suppression is a crucial problem in speech communications. The intelligibility of the speech signal will be degraded by strong reverberation. This paper presents a novel signal processing scheme that offers an improved solution in reducing the effect of interference caused due to reverberation. It is based on the combination of empirical mode decomposition (EMD) and adaptive boost...