The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system

Authors : Mahmut HEKİM

Pages : 285-297

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Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :A novel feature extraction called discretization-based entropy is proposed for use in the classification of EEG signals. To this end, EEG signals are decomposed into frequency subbands using the discrete wavelet transform insert ignore into journalissuearticles values(DWT);, the coefficients of these subbands are discretized into the desired number of intervals using the discretization method, the entropy values of the discretized subbands are calculated using the Shannon entropy method, and these are then used as the inputs of the adaptive neuro-fuzzy inference system insert ignore into journalissuearticles values(ANFIS);. The equal width discretization insert ignore into journalissuearticles values(EWD); and equal frequency discretization insert ignore into journalissuearticles values(EFD); methods are used for the discretization. In order to evaluate their performances in terms of classification accuracy, three different experiments are implemented using different combinations of healthy segments, epileptic seizure-free segments, and epileptic seizure segments. The experiments show that the EWD-based entropy approach achieves higher classification accuracy rates than the EFD-based entropy approach.
Keywords : EEG signals, discrete wavelet transform DWT, discretization based entropy, adaptive neuro fuzzy inference system ANFIS,