Forked hybrid architecture combining 1D-CNN and quantum convolutional networks for EEG signal classification

Detecting epileptic seizures from electroencephalogram (EEG) signals presents a significant challenge due to the nonlinear and non-stationary nature of brain activity. In this study, we propose a hybrid classification framework that integrates quantum-assisted and classical deep learning techniques to enhance detection accuracy and model interpretability. First, discrete wavelet transform (DWT) is applied to extract multiscale time–frequency features from raw EEG signals. Subsequently, three comparative models are developed: a conventional 1D-CNN model, an IQCNN model incorporating a parametric quantum circuit, and a novel Forked Enhanced Model that processes EEG features simultaneously through both 1D-CNN and IQCNN branches and combines them in a fully connected layer. The proposed hybrid model leverages the expressive power of quantum entanglement while preserving the robustness of classical deep learning. Evaluations using 5-fold stratified cross-validation on a real EEG dataset show that the forked architecture outperforms both classical CNNs and quantum-only based models in terms of accuracy, F1 score, precision, recall, number of incorrectly classified subjects and AUC. In this study, the best result was obtained with the Denoise IQCNNNet hybrid model. The accuracy of this model is 97.25%, precision 92.74%, recall 93.66%, F-1 score 93.15%, AUC 99.54%, number of incorrectly classified subjects are 57. This study demonstrates the feasibility and potential of hybrid quantum-classical networks in biomedical signal processing and provides a foundation for real-time clinical seizure monitoring systems.