Classifying High-Frequency Oscillations by Morphologic Contrast to Background, With Surgical Outcome Correlates

Ictal high-frequency oscillations (HFOs) are a reliable indicator of a seizure onset zone for intracranial EEG recordings. Interictal HFOs often are also observed and may be a useful biomarker to supplement ictal data, but distinguishing pathologic from physiologic HFOs continues to be a challenging task. We present a method of classifying HFOs based on morphologic contrast to the background.

We retrospectively screened 31 consecutive patients who underwent intracranial recordings for epilepsy at Stanford Medical Center during a 2-year period, and 13 patients met the criteria for inclusion. Interictal EEG data were analyzed using an automated event detector followed by morphologic feature extraction and k-means clustering. Instead of only using event features, the algorithm also incorporated features of the background adjacent to the events. High-frequency oscillations with higher morphologic contrast to the background were labeled as pathologic, and "hotspots" with the most active pathologic HFOs were identified and compared with clinically determined seizure onset zones.

Clustering with contrast features produced groups with better separation and more consistent boundaries. Eleven of the 13 patients proceeded to surgery, and patients whose hotspots matched seizure onset zones had better outcomes, with 4 out of 5 "match" patients having no disabling seizures at 1+ year postoperatively (Engel I or International League Against Epilepsy Class 1-2), while all "mismatch" patients continued to have disabling seizures (Fisher exact test P -value = 0.015).

High-frequency oscillations with higher contrast to background more likely represent paroxysmal bursts of pathologic activity. Patients with HFO hotspots outside of identified seizure onset zones may not respond as well to surgery.