Cross-Patient Epileptic Seizure Labeling via an Unsupervised Hybrid Anomaly Detection Gate

Continuous scalp EEG monitoring enables automated epileptic seizure detection, but extreme class imbalance, inter-patient covariate shifts, and intra-subject data leakage heavily hinder clinical deployment. This study introduces an unsupervised, cross-patient feature selection and patient-independent one-class anomaly detection under Leave-One-Group-Out (LOGO) cross-validation enhanced by a novel One-Class Hybrid (G-Cramer’s V) Statistical Gate to resolve these gaps. The architecture models normal baseline neural activity as the solitary learned class, employing a dynamic feature gate that maximizes baseline purity via a One-Class Gini Index and drives robust cross-patient generalization through a Cramer’s V filter. To eliminate patient-level data leakage and ensure reliable cross-patient performance, a strict Leave-One-Group-Out (LOGO) cross-validation protocol is enforced. Benchmarked on the open-source CHB-MIT database against five core unsupervised estimators (OCSVM, kNN, LNND, LOF, IF) and a reference Morlet Wavelet baseline, the proposed Local Outlier Factor (LOF) estimator utilizing a Gated Fourier framework delivers high precision and efficiency. It achieves a mean cohort F1-score of 0.79, a precision of 78%, and a False Alarm Rate of 1.5 alarms per hour on completely unseen patients, while simultaneously yielding a 35.5% reduction in feature-extraction latency.