This paper demonstrates the predictive superiority of discrete wavelettransform (DWT) over previously used methods of feature extraction in thediagnosis of epileptic seizures from EEG data . The mean-differences are statistically significant respectively in the imbalanced and balanced dataset . The results also highlight that MFCC performs less than all the DWT used in this study and that, MFCC does less than . all the . DWT uses in the balanced or theimbalanced dataset, the feature extraction techniques, the models, and the interaction between them have a statistically significant effect on the accuracy of the classification accuracy .

Author(s) : Cyrille Feudjio, Victoire Djimna Noyum, Younous Perieukeu Mofendjou, Rockefeller, Ernest Fokoué

Links : PDF - Abstract

Code :
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Keywords : dwt - significant - feature - eeg - seizures -

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