The empirical mode decomposition (EMD) was a method pioneered by Huang et al [8] as an alternative technique to the traditional Fourier and wavelet techniques for studying signals. It decomposes a signal into several components called intrinsic mode functions (IMF), which have shown to admit better behaved instantaneous frequencies via Hilbert transforms. In this paper we propose an alternative...

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.

Wigner-Ville Distribution (WVD) possesses high time-frequency concentration and resolutions, but brings serious cross-terms in process of a multi-component signal to distort greatly the result of timefrequency analysis. Because of this the use of the WVD is limited for many applications. In this paper a combined method is proposed: Empirical Mode Decomposition (EMD) is first used to decompose t...

In this study, a new speckle reduction method was proposed in terms of by Bidimensional Empirical Mode Decomposition (BEMD). In this method, the SAR image containing speckle noise was decomposed into a number of elementary components by using BEMD and then the extremal points are done the boundary equivalent extension after screening and the residual continue to be done the boundary equivalent ...

For non-destructive evaluation (NDE) of concrete, efficient and accurate imaging techniques are needed for a reliable evaluation of safety and serviceability of structures. Imaging of concrete structures is considered a very challenging task since it is a non-homogeneous material. In this paper, the acoustic (stress wave) technique was applied by using Ultrasonics and Synthetic Aperture Focusin...

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang tran...

An efficient algorithm based on Empirical Mode Decomposition (EMD) de-noising using soft 10 threshold techniques for accurate doppler profile detection and Signal to Noise Ratio (SNR) improvement of 11 MST Radar Signals is discussed in this paper. Hilbert Huang Transform (HHT) is a time-frequency analysis 12 technique for processing radar echoes which constitutes EMD process that decomposes the...

Objective: Exploring resting-state functional networks using functional magnetic resonance imaging (fMRI) is a hot topic in the field of brain functions. Previous studies suggested that the frequency dependence between blood oxygen level dependent (BOLD) signals may convey meaningful information regarding interactions between brain regions. Methods: In this article, we introduced a novel freque...

Objective. This research provides objective and quantitative parameters of the traditional Chinese medicine (TCM) pulse conditions for distinguishing between patients with the coronary heart disease (CHD) and normal people by using the proposed classification approach based on Hilbert-Huang transform (HHT) and random forest. Methods. The energy and the sample entropy features were extracted by ...

The fault diagnosis precision for rolling bearings under variable conditions has always been unsatisfactory. For solving this problem, a feature extraction method combing the Hilbert-Huang transform with singular value decomposition was proposed in this paper. The method includes three steps. Firstly, instantaneous amplitude matrices were obtained by Hilbert-Huang transform from rolling bearing...