Clinical-Grade EEG Seizure Detection Achieving 93.9% Precision: First Hospital-Deployable System Through Multi-Domain Feature Engineering and Ensemble Deep Learning

Automated electroencephalogram (EEG) seizure detection remains limited by precision rates of 35-70%, preventing clinical deployment due to excessive false alarms. This study presents the first automated system to achieve clinical-grade precision (93.9%) on the CHB-MIT dataset, directly addressing the deployment barrier that has prevented hospital implementation. Our methodology integrates an unprecedented 2,138-dimensional multi-domain feature space combining time-frequency analysis, spectral characteristics, statistical measures , and connectivity patterns, processed through a three-model ensemble (Enhanced XGBoost, LightGBM, Transformer-GRU) with focal loss optimization. Patient-wise cross-validation demonstrates exceptional performance: F1-score of 0.948, precision of 93.9%, recall of 95.8%, and AUC of 0.998. Comprehensive ablation studies reveal that multi-domain feature engineering contributes 57% of performance gains, SMOTE class balancing adds 35%, ensemble methodology provides 16%, and focal loss optimization delivers final clinical-grade refinement. The system achieves 6.1% false positive rate (0.89 false alarms/hour) with <100ms inference latency, meeting stringent clinical deployment requirements. This breakthrough represents the first clinically-viable automated seizure detection system suitable for continuous hospital monitoring, with complete code availability ensuring reproducibility.