Automatic Epileptic Seizure Detection via AttentionBased CNNBiRNN Epileptic seizure detection with multichannel electroencephalography (EEG) signals is a commonly used method, but it is tedious and errorprone to manually detect seizures through EEG signals. In this work, we propose an endtoend deep neural network called attentionbased CNNBiRNN for automatic seizure detection. Attentionbased CNNBiRNN mainly consists of three parts: the multiscale convolution model, the attention model, and the multistream bidirectional recurrent model. Original signals are firstly sent to the multiscale convolution model to extract multiscale features. Then the attention model exploits the differences among channels for seizure detection. Afterwards, the robust temporal features are obtained by the multistream bidirectional recurrent model, and are further fed into a fully connected layer for classification. Moreover, a channel dropout method is proposed, for the model training stage, to obtain inconspicuous characteristics from all the channels of a certain EEG signal. The results on the dataset of CHBMIT demonstrate that our approach outperforms stateoftheart approaches in terms of both sensitivity and specificity. Furthermore, with the channel dropout method, our approach is shown to have a powerful ability of handling EEG signals with missing channels and different channels.

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 Nonseizure 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 pretrained 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 nonseizure 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 stateoftheart 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.