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This paper evaluates the approach of imaging timeseries data such as EEG inthe diagnosis of epilepsy through Deep Neural Network (DNN). EEG signal istransformed into an RGB image using Gramian Angular Summation Field (GASF).Many such EEG epochs are transformed into GASF images for the normal and focalEEG signals. Then, some of the widely used Deep Neural Networks for imageclassification problems are used here to detect the focal GASF images. Threepre-trained DNN such as the AlexNet, VGG16, and VGG19 are validated forepilepsy detection based on the transfer learning approach. Furthermore, thetextural features are extracted from GASF images, and prominent features areselected for a multilayer Artificial Neural Network (ANN) classifier. Lastly, aCustom Convolutional Neural Network (CNN) with three CNN layers, BatchNormalization, Max-pooling layer, and Dense layers, is proposed for epilepsydiagnosis from GASF images. The results of this paper show that the Custom CNNmodel was able to discriminate against the focal and normal GASF images with anaverage 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 OperatingCharacteristic (ROC) curve is 0.92 for the Custom CNN model. This papersuggests that Deep Learning methods widely used in image classificationproblems can be an alternative approach for epilepsy detection from EEG signalsthrough GASF images.

Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis