  This paper evaluates the approach of imaging timeseries data such as EEG in
the diagnosis of epilepsy through Deep Neural Network (DNN). EEG signal is
transformed into an RGB image using Gramian Angular Summation Field (GASF).
Many such EEG epochs are transformed into GASF images for the normal and focal
EEG signals. Then, some of the widely used Deep Neural Networks for image
classification problems are used here to detect the focal GASF images. Three
pre-trained DNN such as the AlexNet, VGG16, and VGG19 are validated for
epilepsy detection based on the transfer learning approach. Furthermore, the
textural features are extracted from GASF images, and prominent features are
selected for a multilayer Artificial Neural Network (ANN) classifier. Lastly, a
Custom Convolutional Neural Network (CNN) with three CNN layers, Batch
Normalization, Max-pooling layer, and Dense layers, is proposed for epilepsy
diagnosis from GASF images. The results of this paper show that the Custom CNN
model was able to discriminate against the focal and normal GASF images with an
average peak Precision of 0.885, Recall of 0.92, and F1-score of 0.90.
Moreover, the Area Under the Curve (AUC) value of the Receiver Operating
Characteristic (ROC) curve is 0.92 for the Custom CNN model. This paper
suggests that Deep Learning methods widely used in image classification
problems can be an alternative approach for epilepsy detection from EEG signals
through GASF images.
