Autoregressive Modeling Based Empirical Mode Decomposition (EMD) for Epileptic Seizures Detection Using EEG Signals

Abstract

Epilepsy is a neurological disorder affecting several millions of humans on earth. Epileptic seizures provoked in major cases by sudden electrical discharges of tremendous brain cells could not be predicted. Hence, automatic seizures detection and classification based on the analysis of electroencephalographic (EEG) signals becomes essential. The purpose of this paper is to propose a new feature extraction method using empirical mode decomposition (EMD) and a multilayer perceptron neural network (MLPNN). The EMD algorithm decomposes a time segment EEG into intrinsic mode functions (IMFs) on which autoregressive (AR) parameters are extracted, combined and fed to the MLPNN classifier. Experimental results carried out on a publicly available dataset, comprising normal, interictal and ictal EEG signals achieved classification accuracy up to 98 %. The outcome of this research is mainly intended to aid practioners in the diagnosis of epileptic portions in the EEG recordings.

Odor and Subject Identification Using Electroencephalography Reaction to Olfactory

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On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN

“…Among them, RBFNN has the advantages of simple structure, strong nonlinear fitting ability, fast approaching and high robustness. Compared with traditional neural networks, it can better overcome the shortcomings of local minimum and slow convergence speed, and is widely used in fault diagnosis, pattern recognition and other fields [23][24][25]. The input characteristic parameters of the BTS are various and the number is large, which undoubtedly greatly increases the complexity of the topology structure of the RBFNN, which increases the training time of the fault diagnosis model and increases the difficulty of network convergence.…”

Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain

“…According to the literature, several methods have been developed for this purpose. Some of the methods are comprised of permutation entropy (15), horizontal visibility graph (HVG) (16), clustering technique (17), linear prediction error energy (18), fractional linear prediction (FLP) error (19), dual-tree complex wavelet transform (DT-CWT) (20), autoregressive modeling (21), tunable-Q wavelet transform (TQWT) (13,22), reconstructed phase space (RPS) (14), second-order difference plot (SODP) (23,24) , and improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT) (25). Most of the latter methods work on the basis of nonlinear features extraction.…”