EpiGraphNet: Epilepsy Recognition Architecture with Graph-Based EEG Analysis

Early and accurate detection of epileptic seizures is crucial for patients’ quality of life and the treatment process. In this context, our study proposes EpiGrafNet, an innovative hybrid model that delivers high performance in epileptic seizure detection using electroencephalography (EEG) signals. The proposed method extracts both local and long-term temporal features from EEG signals via a one-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) module; these features are then transformed into a graph structure by the utilization of correlation connection matrices (CCM), and integrated with graph convolutional neural networks (GCN). Experiments conducted with different sparsity values demonstrate that the developed model achieves superior accuracy, recall, precision, and F1 scores in both binary and multi-class epileptic seizure detection tasks, indicating that EpiGrafNet provides higher and more reliable results compared to existing methods. This approach, by effectively modeling the complex spatial and temporal relationships in EEG signals, significantly contributes to the development of automatic epileptic seizure detection systems in clinical applications.