The detection of epileptic seizures becomes increasingly important because of the widespread of this disease all over the world. Early detection of epileptic seizures helps the patient to manage epilepsy. This paper introduces a detection system for epileptic seizures that implements a Short-Time Fourier Transform (STFT) for denoising the Electroencephalogram (EEG) signal and Wavelet Transform (WT) for features extraction. Four EEG types (i.e Healthy people, Epileptic people during the seizure-free interval (Interictal), Epileptic people during seizure interval (Focal) and Epileptic people during seizure interval (Nonfocal)) are classiﬁed by using a Multi-Layer Perceptron Neural Network (MLPNN) classiﬁer. The used dataset was enlarged to be 1050 instead of 500 in the available detection systems. The integration of STFT and WT has an important impact on improving the detection accuracy. The accuracy of the proposed system is 94.4 % which signiﬁcantly outperforms the previous systems in terms of dataset size and the number of classiﬁed EEG signal types.
Digital Object Identifier (DOI)
AL-Bokhity, B.; Nashat, Dalia; and M. Nazmy, T.
"Accuracy Enhancement of the Epileptic Seizure Detection in EEG Signals,"
Applied Mathematics & Information Sciences: Vol. 11:
6, Article 16.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol11/iss6/16