Automatic epileptic EEG detection using DT-CWT-based non-linear features

The epilepsy is a type of common neurological disorder plaguing many people around the world. A novel method based on the dual-tree complex wavelet transform (DT-CWT), in this study, is proposed to develop a reliable diagnosis method for the epileptic EEG detection. We explore the ability of DT-CWT to decompose the original EEG into five constituent sub-bands, which are associated with non-linear features such as the Hurst exponent (H), Fractal Dimension (FD) and Permutation Entropy (PE). Furthermore, influences of different filter types on the DT-CWT are considered in this study as well. With these features, the support vector machine (SVM) configured with filters of the near-symmetric 13/19 tap filters (NS 13/19) and Q-shift 14/14 tap filters (QS 14/14) is found to achieve the preferable classification accuracy of 98.87%, which is visibly higher than that with discrete wavelet transform (DWT)-based features. Results demonstrate that the technique proposed by us can not only provide significant performance with less computational cost but also can implement simply. It will be a potential method for practical applications extended to the development of a real-time brain monitoring system.