Automated Detection of Ripple Oscillations in Long-Term Scalp EEG from Patients with Infantile Spasms

Abstract

ObjectiveScalp high frequency oscillations (HFOs) are a promising biomarker of epileptogenicity in infantile spasms (IS) and many other epilepsy syndromes, but prior studies have relied on visual analysis of short segments of data due to the prevalence of artifacts in EEG. Therefore, we set out to develop a fully automated method of HFO detection that can be applied to large datasets, and we sought to robustly characterize the rate and spatial distribution of HFOs in IS. MethodsWe prospectively collected long-term scalp EEG data from 13 subjects with IS and 18 healthy controls. For patients with IS, recording began prior to diagnosis and continued through initiation of treatment with adenocorticotropic hormone (ACTH). The median analyzable EEG duration was 18.2 hours for controls and 83.9 hours for IS subjects ([~]1300 hours total). Ripples (80-250 Hz) were detected in all EEG data using an automated algorithm. ResultsHFO rates were substantially higher in patients with IS compared to controls. In IS patients, HFO rates were higher during sleep compared to wakefulness (median 5.5/min and 2.9/min, respectively; p =0.002); controls did not exhibit a difference in HFO rate between sleep and wakefulness (median 0.98/min and 0.82/min, respectively). Spatially, the difference between IS patients and controls was most salient in the central/posterior parasaggital region, where very few HFOs were detected in controls. In IS subjects, ACTH therapy significantly decreased the rate of HFOs. DiscussionHere we show for the first time that a fully automated algorithm can be used to detect HFOs in long-term scalp EEG, and the results are accurate enough to clearly discriminate healthy subjects from those with IS. We also provide a detailed characterization of the spatial distribution and rates of HFOs associated with infantile spasms, which may have relevance for diagnosis and assessment of treatment response.