An epileptic seizure, a disorder in brain functionality,
happens when electrical bursts spread across the brain,
causing the person to lose control or consciousness. Predicting
epileptic seizures before they occur is useful for seizure
prevention with medicine or for neural pre-surgical planning.
Machine learning and computational methods are used to predict
epileptic seizures from electroencephalogram (EEG) recordings.
However, noise removal and feature extraction in EEG data are
two important challenges that have a negative impact on the
effectiveness of both time and true positive prediction rate. We
offer a model in this paper that provides a reliable strategy for
both preprocessing and feature extraction. Our model is based
on a two-dimensional Convolution Neural Network (CNN), with
EEG input provided to the CNN in the form of two-dimensional
images. On the University of Bonn data set’s normal vs intericatl
vs ictal instance, our proposed system achieved 97.8 percent
accuracy and over 97 percent for the other parameters, which
include precision, recall, F1-score, and ROC-AUC. Our findings
are reproducible using the code available on github .