Detection of Epileptic EEG Signal Using Improved Local Pattern Transformation Methods

This paper aims to introduce efficient computer-aided techniques for automated epileptic diagnosis using the features based on improved local pattern transformation methods (LPT). To analyze electroencephalographic (EEG) signal, three techniques, namely one-dimensional local neighbor descriptive count, one-dimensional local gradient count and one-dimensional local binary count, are proposed in this work. Further, a signature point-based improved LPT approach is introduced for effectual classification of EEG signals. The features are computed at the signature points of the EEG signals, which are detected by using the difference of Gaussian pyramid. The features extracted from the signature points of the EEG signals are fed into artificial neural network (ANN) classifier for the discrimination of EEG signals. In this paper, seventeen different classification cases based on six different experimental cases are evaluated using the University of Bonn EEG database. Experimental results show that high classification accuracy for all the cases is achieved using the proposed approach and it also compares favorably to other state-of-the-art methods.