Maximum and minimum computed across channels is used to monitor the Electroencephalogram signals for possible change of the eye state. Upon detection of a possible change, the last two seconds of the signal is passed through Multivariate Empirical Mode Decomposition and relevant features are extracted. The features are then fed into Logistic Regression and Artificial Neural Network classifiers ...

In this paper Empirical Mode Decomposition (EMD)-based features from single-channel electroencephalographic (EEG) data are proposed for rat’s sleep state classification. The classification performances of the EMD-based features and some classical power spectrum density (PSD)-based features are compared. Supported by experiments on real EEG data, we demonstrate that classification performances c...

Towards developing a rigorous mathematical theory for Empirical Mode Decomposition (EMD), we provide an overview of the algorithm and introduce a corresponding operator, attempting a preliminary study. We prove that the EMD is nonlinear, we identify the major reason of its nonlinearity, and we introduce the related concept of consistency, which we show the EMD does not satisfy either. & 2012 Pu...

[1] Sea level data from the Chesapeake Bay are used to test a novel new analysis method for studies of sea level rise (SLR). The method, based on Empirical Mode Decomposition and Hilbert-Huang Transformation, separates the sea level trend from other oscillating modes and reveals how the mean sea level changes over time. Bootstrap calculations test the robustness of the method and provide confid...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

In this work, a new instantaneous fundamental frequency extraction method is presented, with the attention especially focused on its robustness for pathological voices processing. It is based on the Ensemble Empirical Mode Decomposition (EEMD) algorithm, which is a completely datadriven method for signal decomposition into a sum of AM FM components, called Intrinsic Mode Functions (IMFs) or mod...

In this paper, we propose a multiscale dependence-based methodology to analyze the dependence structure and to estimate the downside portfolio risk measures in the energy markets. More specifically, under this methodology, we formulate a new bivariate Empirical Mode Decomposition (EMD) copula based approach to analyze and model the multiscale dependence structure in the energy markets. The prop...

The paper presents a novel method for reconstruction of missing data in time series. The method is based on the decomposition of known parts of time series into monocomponents (Intrinsic Mode Functions, IMF) using Empirical Mode Decomposition (EMD), construction of prediction models for each IMF using known parts of times series and their composition using weighted average. We demonstrate the e...

A bidimensional empirical mode decomposition (BEMD) program on a MATLAB platform was effectively used to handle gravity signals for the Tongshi gold field. This yielded a two-dimensional intrinsic mode function (IMF3) image that meticulously depicts the spatial distribution relationship between various gold deposits and the different geological units of the gold field. By combining the IMF3 ima...

Considering the problem of extracting a trend from a time series, we propose a novel approach based on empirical mode decomposition (EMD), called EMD trend filtering. The rationale is that EMD is a completely data-driven technique, which offers the possibility of estimating a trend of arbitrary shape as a sum of low-frequency intrinsic mode functions produced by the EMD. Based on an empirical a...