This paper presents a novel deep-learning model for classifying epileptic EEG signals with an emphasis on improving accuracy and generalizability across datasets and seizure types. The authors develop a network called RDPNet that uses residual convolution modules with a dilated convolution pyramid, dual-pathway fusion of pooled features, and entropy-based features (differential entropy). The authors evaluate the approach primarily on two benchmark datasets, the University of Bonn dataset and Temple University Hospital Seizure Corpus (TUSZ). RDPNet achieves nearly perfect results for many classification tasks, including 99.56-100% accuracy for a binary classification task in the Bonn dataset, 99.29-99.79% accuracy for a ternary task, and 95.10% accuracy on a five-class task. On the TUSZ dataset, also thought to be more difficult and realistic, the model achieves a weighted F₁ score of 95.72% across seven seizure types. Model comparisons using ablation experiments are informative, and the authors conclude that the dilated convolution pyramid module and differential entropy feature fusion were especially important. In the discussion, the authors consider the role of each component (e.g., residual blocks supporting local detail, dilated convolutions supporting long-range temporal dependencies, and entropy features for statistical complexity) and trade-offs exploring temporal window length vs. computational cost. The authors note limitations and possible future developments, including further evaluations in more diverse clinical settings and developing more computationally efficient models.