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In this paper, we attempt to analyze the performance of the Empirical Mode Decomposition (EMD) for discriminating epileptic seizure data from the normal data. The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and nonstationary time series. The main idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode functions...
A novel fault diagnosis method based on empirical mode decomposition (EMD) and multi-features fusion support vector machine (SVM) is proposed in this paper. Firstly, the given signal is decomposed into a number of intrinsic mode functions (IMFs) by EMD. Choose the first several energy-dominating IMFs, and extract their wavelet packet features, respectively. So, a series of feature sub-spaces are obtained...
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