Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms

• Published in: Journal of neural engineering Vol. 18; no. 1; p. 16018
• Main Authors: McCrimmon, Colin M, Riba, Aliza, Garner, Cristal, Maser, Amy L, Phillips, Donald J, Steenari, Maija, Shrey, Daniel W, Lopour, Beth A
• Format: Journal Article
• Language: English
• Published: England 01.02.2021
• Subjects: epilepsy; high-frequency oscillation; west syndrome; prospective; scalp electroencephalography; pediatric
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2552/abcc7e

Scalp 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. Here we set out to robustly characterize the rate and spatial distribution of HFOs in large datasets from IS subjects using fully automated HFO detection techniques. We prospectively collected long-term scalp EEG data from 12 subjects with IS and 18 healthy controls. For patients with IS, recording began prior to diagnosis and continued through initiation of treatment with adrenocorticotropic hormone (ACTH). The median analyzable EEG duration was 18.2 h for controls and 84.5 h for IS subjects (∼1300 h total). Ripples (80-250 Hz) were detected in all EEG data using an automated algorithm. HFO 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;  = 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, IS patients exhibited significantly higher rates of HFOs in the posterior parasaggital region and significantly lower HFO rates in frontal channels, and this difference was more pronounced during sleep. In IS subjects, ACTH therapy significantly decreased the rate of HFOs. Here we provide a detailed characterization of the spatial distribution and rates of HFOs associated with IS, which may have relevance for diagnosis and assessment of treatment response. We also demonstrate that our fully automated algorithm can be used to detect HFOs in long-term scalp EEG with sufficient accuracy to clearly discriminate healthy subjects from those with IS.