Abstracts

Rationale: High frequency oscillations have been proposed as a clinically important indicator of epileptic networks. However, manual detection of these events is difficult, time consuming, and subjective, especially in the scalp electroencephalogram (EEG), thus hindering further clinical exploration and application. We recently developed a semi-automated method to detect fast oscillations that co-occur with interictal epileptiform discharges (spike-ripple events). Here we apply this new method to a population of patients with benign epilepsy with centrotemporal spikes (BECTS, n=19) and healthy controls (n=3). Methods: The semi-automated method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. Visual analysis of the candidate events blinded to clinical information was then performed by two reviewers using between 3 and 55 minutes of artifact free data for a population of 22 subjects. Results: We show that the semi-automated method successfully detects spike-ripple events in patients with a recent seizure within 12 months (n=8), but not in patients who were seizure free for > 12 months either off medication (n=7) or on medication (n=4), and healthy controls (n=3). Overall, the method performs well in distinguishing patients with a recent seizure from those without (sensitivity 87.5%, specificity 92.3%). We also show that, among children with BECTS, the detection rate decreases as the time from a patient’s most recent seizure increases. Conclusions: Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. We apply a recently developed spike-ripple detector to a population of children with BECTS and healthy subjects, and show that the detected events separate the two groups. These results provide evidence that spike-ripple events correlate with seizure risk and that rapid, semi-automated detection in multielectrode scalp EEG recordings is tractable. Funding: NINDS K23NS092923