The researchers present an EEG-based lightweight seizure classification model designed for real-time deployment in clinical settings. The goal is to automatically alert hospital staff when a seizure is imminent while overcoming key limitations of existing methods. Current models typically require large datasets and contain many parameters, resulting in high computational overhead and slow prediction times—making them impractical for real-time use. The proposed model addresses this by introducing a compact, low-parameter, end-to-end network that directly processes raw EEG time-series data. A second challenge is that EEG signals are inconsistently labeled, and automated pseudo-labeling strategies are lacking. The authors address this by implementing a statistical model that quantifies seizure activity in terms of measurable energy changes over time. These statistical labels then serve as ground truth for training the lightweight deep learning network (LCNet), enabling it to learn high-level features from noisy raw data. Finally, the authors integrate a Hybrid Enhancement Model (HEM) to mitigate the inherent challenges of EEG data. The HEM removes low-response signals prone to noise, applies a channel permutation algorithm to prevent overreliance on any single channel, and uses a data augmentation strategy based on 30-second sliding windows. This segmentation helps the model capture local temporal features and reduces instability caused by nonstationary long-term EEG patterns.