The electroencephalogram (EEG) signals are basically electrophysiological signals that are normally used to access the condition of brain. Epilepsy is one of the brain's disorder. Automated diagnosis of epilepsy can be done by measuring and analyzing the nonlinear and non-stationary trends in EEG signals. This paper introduces a new diagnostic approach for analysis and classification of seizure and seizure-free EEG signals. In time-scale domain, the tunable-Q wavelet transform (TQWT) can reliably represent the sparsity in oscillatory signals. The proposed methodology begins with application of TQWT to efficiently characterize the non-stationary behavior and sparsity of EEG signals. TQWT decomposes the considered signals into a valuable set of band-limited signals termed as sub-bands for better feature extraction. Kraskov entropy is a nonlinear parameter to detect the presence of nonlinear trends in the signals. After decomposition, Kraskov entropy is computed from the specific sub-band as a decisive feature in order to discriminate seizure-free from the epileptic seizure EEG signals. Subsequently, obtained feature vectors are used for classifying the seizure and seizure-free EEG signals using the least square support vector machine (LS-SVM) classifier. While doing analysis, it has been observed that value of proposed Kraskov entropy based feature is significantly higher for seizure EEG signals as compared to that of seizure-free EEG signals. Furthermore, the experimental results of this work has demonstrated significant values of classification accuracy, sensitivity, specificity and Matthew's correlation coefficient. It is noteworthy that proposed framework uses single feature to diagnose the epilepsy accurately. Also the application of the proposed work on EEG data from the University of Bonn, Germany highlights the consistency and in some cases superiority of the proposed method over other popular methods.
['Shivnarayan Patidar', 'Trilochan Panigrahi']
Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals