Integrating Spatial-Temporal Channel Reliability with AI for Robust High-Frequency Oscillation Detection in...

Reliable detection of high-frequency oscillations (HFOs) in intracranial EEG (iEEG) is critical for localizing the seizure onset zone (SOZ). However, artifact contamination severely impairs detection specificity and contributes to false positives.

We developed a reliability guided HFO analysis pipeline that targets artifact elimination through a multi-level strategy, including event level denoising, channel signal quality assessment, and segment level temporal reliability analysis. The classifier was trained on 22 subjects using manual annotations of reliable versus unreliable data and validated using leave-one-subject-out cross-validation (LOSO-CV).

The final pipeline was further applied to an additional cohort of 8 independent subjects to evaluate generalizability and clinical utility. SOZ localization accuracy based on HFOs retained after event-level denoising improved from 66% (baseline) to 82%, and further to 93% after applying channel and segment reliability filters. ROC analysis confirmed that reliability-based filtering enhanced separation between SOZ and non-SOZ channels, improving clinical specificity.

Incorporating spatial and temporal contextual information, specifically by evaluating signal quality across neighboring channels and consistency across adjacent time windows, significantly enhances the ability to distinguish true HFOs from artifacts. This approach improves both the robustness and clinical accuracy of HFO-based SOZ localization.