A simplified method for classification of epileptic EEG signals
by Garima Chandel; P.P. Muhammed Shanir; Omar Farooq; Yusuf Uzzaman Khan
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 25, No. 1, 2017

Abstract: Epileptic seizures occur randomly and are difficult to identify in Electroencephalogram (EEG) recording with multiple channels. Most researchers have used large dimension features, complex transformation techniques and non-linear classifier. A new algorithm based on Mean Absolute Deviation (MAD) using lower feature vector dimension and linear classifier is proposed for automatic seizure detection using EEG signals. The proposed method calculates MAD of each channel on frame consisting of 256 samples. In order to reduce the dimension of the feature, mean and maximum value of the MAD for all channels were selected as discriminating parameters. The proposed algorithm is tested on a publicly available Bonn University EEG database for three cases. The accuracy of the algorithm was 100% in all the considered problems. The proposed work outperforms in terms of complexity with respect to the other available state-of-the-art method on the same database. It results in reduction of number of features per frame with less complexity for all considered problems.

Online publication date: Tue, 12-Sep-2017

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