Wearable electroencephalogram (EEG) devices offer promising solutions for continuous seizure monitoring in out-of-hospital settings. However, the adoption of medical-grade wearable devices remains limited by the power consumption demands of edge computing, where each active EEG channel contributes to energy expenditure through signal processing and feature extraction operations. Optimizing algorithms through feature reduction may extend battery life without compromising diagnostic reliability. This study evaluated a feature reduction strategy for a 100-tree AdaBoost ensemble model applied to binary seizure detection using the Bangalore EEG Epilepsy Dataset, comprising recordings from 60 subjects. A patient-aware data partitioning scheme (70/15/15 split) was implemented to ensure complete separation of patient identities across training, validation, and testing subsets. Feature importance scores were extracted from the trained ensemble to identify less discriminative EEG channels, and classification performance was compared between a baseline 16-channel configuration and a reduced 12-channel configuration following removal of the four least important channels. The baseline 16-channel model achieved 97.56% test accuracy, 97.29% sensitivity, 98.50% specificity, and a 2.71% seizure miss rate. Following the removal of channels x₅, x₉, x₁₁, and x₁₃, the reduced 12-channel model achieved 97.11% test accuracy, 96.71% sensitivity, 98.50% specificity, and a 3.29% seizure miss rate. The 25% reduction in active channels resulted in only a 0.45 percentage-point decrease in accuracy while yielding an estimated 33.33% theoretical extension in battery operating duration. Strategic channel reduction based on feature importance analysis can improve computational efficiency while maintaining comparable seizure detection performance. These findings support the development of energy-efficient, long-term ambulatory EEG monitoring devices that balance diagnostic reliability with extended operational duration.