Unsupervised classification of high-frequency oscillations in human neocortical epilepsy and control patients

High-frequency oscillations (HFOs) have been observed in animal and human intracranial recordings during both normal and aberrant brain states. It has been proposed that the relationship between subclasses of these oscillations can be used to identify epileptic brain. Studies of HFOs in epilepsy have been hampered by selection bias arising primarily out of the need to reduce the volume of data so that clinicians can manually review it. In this study, we introduce an algorithm for detecting and classifying these signals automatically and demonstrate the tractability of analyzing a data set of unprecedented size, over 31,000 channel-hours of intracranial electroencephalographic (iEEG) recordings from micro- and macroelectrodes in humans. Using an unsupervised approach that does not presuppose a specific number of clusters in the data, we show direct evidence for the existence of distinct classes of transient oscillations within the 100- to 500-Hz frequency range in a population of nine neocortical epilepsy patients and two controls. The number of classes we find, four (three plus one putative artifact class), is consistent with prior studies that identify "ripple" and "fast ripple" oscillations using human-intensive methods and, additionally, identifies a less examined class of mixed-frequency events.