Data-Driven Hyperparameter Tuning of High-Frequency Oscillation Detection for Seizure-Onset Zone Localization

High-frequency oscillations (HFOs) are promising biomarkers for localizing the seizure onset zone (SOZ) in drug-resistant epilepsy. Automatic HFO detectors often depend on specific parameter choices, limiting effectiveness across diverse datasets. This study optimizes a published amplitude-threshold-based detector using a data-driven approach on intracranial EEG recordings from 20 patients at four epilepsy centers. We use the Tree-structured Parzen Estimator (TPE) to efficiently search the parameter space, guided by a new objective function, the SOZ Detection Quality (SDQ) score, which balances SOZ localization precision with the number of detected events in the SOZ. In the training cohort, the optimized detector increased the proportion of detections in SOZ channels from 49% to 61% and improved AUROC from 0.78 to 0.89 while largely preserving the SOZ detection counts. Similar improvements were observed in the test cohort, where detections in SOZ rose from 45% to 62%, and AUROC increased from 0.92 to 0.95. In both cohorts, detections outside the SOZ were significantly reduced, enhancing spatial specificity without compromising sensitivity. Theoretically, our TPE-based optimization achieved over 2,958×speedup over a grid search. These results show an effective data-driven parameter tuning approach that improves HFO biomarker reliability and supports their use in clinical planning for epilepsy surgery.