  This paper demonstrates the predictive superiority of discrete wavelet
transform (DWT) over previously used methods of feature extraction in the
diagnosis of epileptic seizures from EEG data. Classification accuracy,
specificity, and sensitivity are used as evaluation metrics. We specifically
show the immense potential of 2 combinations (DWT-db4 combined with SVM and
DWT-db2 combined with RF) as compared to others when it comes to diagnosing
epileptic seizures either in the balanced or the imbalanced dataset. The
results also highlight that MFCC performs less than all the DWT used in this
study and that, The mean-differences are statistically significant respectively
in the imbalanced and balanced dataset. Finally, either in the balanced or the
imbalanced dataset, the feature extraction techniques, the models, and the
interaction between them have a statistically significant effect on the
classification accuracy.
