Epilepsy Detection Based on Graph Convolutional Neural Network and Transformer

Abstract

Epilepsy detection is a critical medical task, but traditional methods face challenges in accuracy and reliability due to the difficulty of EEG data acquisition and the limitation of the number of sample seizures. To overcome these challenges, this paper proposes a new model for epilepsy detection that combines Graph Convolutional Neural Network (Graph Convolutional Network, GCN) and Transformer, aiming to significantly improve the accuracy and sensitivity of detection. The core of the model adopts GCN, which utilizes its powerful inter-node relationship capturing capability and graph feature learning mechanism. However, due to the limitation of traditional GCN in integrating global features, this model incorporates the Transformer structure to enhance global feature aggregation and reduce irrelevant feature interactions. After multiple rounds of testing of the GHB-MIT dataset, the model demonstrated excellent performance, with an average sensitivity of 92.97%, specificity of 94.60%, and accuracy of 94.59%, which was significantly better than the traditional method. Further comparison with the latest literature also confirms the advantages of the present method. In summary, the epilepsy detection model we developed based on graph convolutional neural network and Transformer not only shows significant improvement in accuracy and sensitivity, but also provides more accurate and reliable technical support for epilepsy diagnosis, which provides a valuable reference for research in related fields.