Publication

Abstract : 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.