Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform

Epilepsy is a neurological disease which is difficult to diagnose accurately. An authentic detection of focal epilepsy will help the clinicians to provide proper treatment for the patients. Generally, focal electroencephalogram (EEG) signals are used to diagnose the epilepsy. In this paper, we have developed an automated system for the detection of focal EEG signals using differencing and flexible analytic wavelet transform (FAWT) methods. The differenced EEG signals are subjected to 15 levels of FAWT. Various entropies namely cross correntropy, Stein's unbiased risk estimate (SURE) entropy, and log energy entropy are extracted from the reconstructed original signal and 16 sub-band signals. The statistically significant features are obtained from Kruskal-Wallis test based on (p < 0.05). K-nearest neighbor (KNN) and least squares support vector machine (LS-SVM) classifiers with different distances and kernels respectively are used for automated diagnosis. In the proposed methodology, we have achieved classification accuracy of 94.41% in detecting focal EEG signals using LS-SVM classifier with ten-fold cross validation strategy.