DDFE-HCNN-E: A Dual-Domain Feature Fusion Hybrid CNN Ensemble Model for Automated Epileptic Seizure Detection

The detection of epileptic seizures based on the electroencephalogram (EEG) signal is extremely critical in clinical diagnosis and long-term monitoring of patients. Nonetheless, the interpretation process of EEG recordings is time-consuming, subjective, and much relies on the expertise, which shows that precise and automated methods of the process must be developed. The paper contains a detailed comparative evaluation of epileptic seizure detection with the help of various pre-trained convolutional neural network (CNN) models, namely, VGG19, ResNet50, DenseNet121, GoogleNet, MobileNetV2, and Efficient-NetB0, on the CHB-MIT EEG dataset. A new Dual-Domain Feature Extraction Hybrid CNN Ensemble (DDFE-HCNN-E) framework is suggested to improve the performance of detection. The approach uses Continuous Wavelet Transform (CWT) to transform EEG signals to time-frequency representations, which are then useful in learning deep features. The suggested model combines ResNet50 with DenseNet121 by using feature-level fusion in addition to a Convolutional Block Attention Module (CBAM) and a Temporal-Aware Inception Module (TAIM) to identify discriminating spatial, spectral, and temporal features of EEG signals. The experimental findings show that the suggested DDFE-HCNN-E model is better than the traditional pre-trained CNN models with an accuracy of 93.4%, sensitivity of 92.8 %, and specificity of 94.1 %. This result demonstrates that hybrid feature fusion and attention-based temporal modeling are effective in the process of reliable and automated epileptic seizure detection.