A Deep Transfer Learning Approach for Seizure Detection Using RGB Features of Epileptic Electroencephalogram Signals

This paper demonstrates an approach based on Deep Transfer Learning for the classification for Seizure and Non-seizure Electroencephalogram (EEG) signals. Recognizing seizure signals in intelligent way is quite important in clinical diagnosis of Epileptic seizure. Various traditional and deep machine learning techniques are employed for this purpose. However, the Epileptic seizure prediction and classification performance is not satisfactory over small EEG dataset using traditional approaches. The Transfer learning approach overcomes this by reusing the pre-trained networks such as googlenet, resnet101 and vgg19 trained on large Image database. This experiment has been done in two phases: (1) RGB image dataset generated for the seizure and non-seizure EEG signals data of University of Bonn using a novel preprocessing technique, (2) we configured googlenet, resnet101 and vgg19 trained networks to learn a new pattern or features from the RGB image Dataset and finally, above mentioned networks have been used for the classification. The use of Vgg19 network shows greater accuracy among the three but takes comparatively more prediction time. We will mainly emphasize on the results obtained from the googlenet, since it provides effective accuracy taking less time for prediction. The proposed method achieved an accuracy of above 99% for a smaller number of epochs and maximum accuracy of 100% when we increase number of epochs. Experimental outcomes show the proposed approach using googlent achieved better performance w.r.t to many state-of-the-art classification algorithms even on the small EEG dataset. In addition, classification performance of our proposed approach has compared with different traditional machine learning techniques over the same input data.