A deep neural network for the classification of epileptic seizures using hierarchical attention mechanism

Electroencephalogram (EEG) is a common diagnostic tool for measuring the seizure activity of the brain. There are many deep learning techniques introduced to analyze EEG. These methods show phenomenal results, although they are limited to computational complexity. Our objective was to develop a novel algorithm that gives maximum classification accuracy with a minor computational complexity. In this view, we have introduced a novel convolutional architecture with an integration of a hierarchical attention mechanism. The model comprises three parts: Feature extraction layer, which uses to extract the convoluted feature map; hierarchical attention layer, which is used to obtain weighted hierarchical feature map; classification layer, which uses weighted features for classification of healthy and seizure subjects. The proposed model can extract significant information from the EEG signal to classify seizure subjects, and it is compared with a few existing deep convolutional algorithms through experimentation. The experimental outcomes show that the proposed model has higher accuracy with less computational time.