An Efficient CNN and RNN Hybrid Model for the Detection of Epileptic Seizures in EEG Signals

Electroencephalogram (EEG) is a widely used supplementary test in epilepsy diagnosis. While traditional methods and deep learning networks have shown considerable success, they often rely on extensive human effort, particularly in feature extraction. This study proposes a hybrid Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) algorithm that can autonomously learn features from EEG data. The RNN component calculates dependency and continuity features from the CNN's intermediate layer output, which are then connected to the final fully connected network for classification prediction. The algorithm was trained and tested on four datasets from the University of Bonn. For binary classification tasks, the proposed CNN-RNN model achieved accuracies of 98.33% on dataset 1 and 100% on dataset 2. When tested on datasets 3 and 4, the model attained 98.00% and 100% accuracy, respectively. Notably, the algorithm demonstrated superior performance on datasets 2 and 4 compared to state-of-the-art methods. These results suggest that the proposed CNN-RNN model offers a promising approach for automated EEG analysis in epilepsy diagnosis, potentially reducing the need for manual feature extraction and improving classification accuracy.