We propose new features for classification of epileptic seizure EEG signals.Features were extracted from PSR of IMFs of EEG signals.We define ellipse area of 2D PSR and IQR of Euclidian distance of 3D PSR as features.LS-SVM classifier has been used for classification with the proposed features.Results were compared with other existing methods studied on the same EEG dataset. Epileptic seizure is the most common disorder of human brain, which is generally detected from electroencephalogram (EEG) signals. In this paper, we have proposed the new features based on the phase space representation (PSR) for classification of epileptic seizure and seizure-free EEG signals. The EEG signals are firstly decomposed using empirical mode decomposition (EMD) and phase space has been reconstructed for obtained intrinsic mode functions (IMFs). For the purpose of classification of epileptic seizure and seizure-free EEG signals, two-dimensional (2D) and three-dimensional (3D) PSRs have been used. New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals. Two measures have been defined namely, 95% confidence ellipse area for 2D PSR and interquartile range (IQR) of the Euclidian distances for 3D PSR of IMFs of EEG signals. These measured parameters show significant difference between epileptic seizure and seizure-free EEG signals. The combination of these measured parameters for different IMFs has been utilized to form the feature set for classification of epileptic seizure EEG signals. Least squares support vector machine (LS-SVM) has been employed for classification of epileptic seizure and seizure-free EEG signals, and its classification performance has been evaluated using different kernels namely, radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernels. Simulation results with various performance parameters of classifier, have been included to show the effectiveness of the proposed method for classification of epileptic seizure and seizure-free EEG signals.

Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions

Variational mode decomposition denoising combined the detrended fluctuation analysis

A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids

Partly ensemble empirical mode decomposition: An improved noise-assisted method for eliminating mode mixing

Changes in the performance of bearings can significantly vary the distribution of internal forces and moments in a structure as a result of environmental or operational loads. The response of a bearing has been traditionally idealized using a linear model but a non-linear representation produces a more accurate picture at the expense of modelling complexity and computational time. In this article, a lead rubber bearing is idealized using the hysteretic Bouc–Wen model. The Hilbert–Huang transform is then employed to characterize the features of the non-linear system from the instantaneous frequencies of the bearing response to a time-varying force. Instantaneous frequencies are also shown to be a useful tool in detecting sudden damage to the bearings simulated by a reduction in the effective stiffness of the force-deformation loop.

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Hai Huang

Papers

Speech pitch determination based on Hilbert-Huang transform