access icon openaccess Efficient method for classification of alcoholic and normal EEG signals using EMD

The electroencephalogram (EEG) signal is an electrical representation of brain's working that reflects various physiological and pathological activities such as alcoholism. Alcohol can affect whole parts of the body but, it particularly affects the brain, heart, liver, and the immune system; its effects on the brain are called brain disorders. Nowadays, automatic identification of alcoholic subjects based on EEG signals has become one of the challenging problems in biomedical research. In this study, an automatic classification method for classifying alcoholic and normal EEG signals, based on empirical mode decomposition (EMD), is proposed. The uniqueness of EMD method is to decompose non-stationary and non-linear signals into a set of stationary intrinsic mode functions (IMFs) that are band limited signals. These IMFs are transformed into analytic representations by applying the Hilbert transform. From these analytic IMFs, various features namely mean, kurtosis, skewness, entropy, and negentropy are extracted; these features are used as input to least squares support vector machines (LS-SVMs) classifier with radial basis function (RBF) kernel and polynomial kernel. The accuracy results achieved for LS-SVM classifier with polynomial and RBF kernels are found to be 96.67 and 97.92%, respectively, which are found to be better as compared with other state-of-the-art methods.

Inspec keywords: medical disorders; Hilbert transforms; electroencephalography; medical signal processing; least squares approximations; signal classification; support vector machines; entropy; radial basis function networks; feature extraction; polynomial approximation

Other keywords: skewness features; nonstationary signals; EMD; entropy; heart; pathological activity; nonlinear signals; Hilbert transform; empirical mode decomposition; kurtosis features; electroencephalogram signal; radial basis function kernel; normal EEG signal classification; liver; stationary intrinsic mode functions; immune system; negentropy; electrical representation; alcoholic EEG signal classification; automatic classification method; feature extraction; physiological activity; polynomial kernel; LS-SVM classifier; brain disorders; band limited signals; least squares support vector machine classifier

Subjects: Biology and medical computing; Signal processing and detection; Numerical approximation and analysis; Electrical activity in neurophysiological processes; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Integral transforms in numerical analysis; Integral transforms in numerical analysis; Function theory, analysis; Electrodiagnostics and other electrical measurement techniques; Bioelectric signals; Knowledge engineering techniques; Neural computing techniques; Digital signal processing

http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2017.0878
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