The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone

Abstract

OBJECTIVE: We hypothesized that high frequency oscillations (HFOs) with irregular amplitude and frequency more specifically reflect epileptogenicity than HFOs with stable amplitude and frequency. METHODS: We developed a fully automatic algorithm to detect HFOs and classify them based on their morphology, with types defined according to regularity in amplitude and frequency: type 1 with regular amplitude and frequency; type 2 with irregular amplitude, which could result from filtering of sharp spikes; type 3 with irregular frequency; and type 4 with irregular amplitude and frequency. We investigated the association of different HFO types with the seizure onset zone (SOZ), resected area and surgical outcome. RESULTS: HFO rates of all types were significantly higher inside the SOZ than outside. HFO types 1 and 2 were strongly correlated to each other and showed the highest rates among all HFOs. Their occurrence was highly associated with the SOZ, resected area and surgical outcome. The automatic detection emulated visual markings with 93% true positives and 57% false detections. CONCLUSIONS: HFO types 1 and 2 similarly reflect epileptogenicity. SIGNIFICANCE: For clinical application, it may not be necessary to separate real HFOs from "false oscillations" produced by the filter effect of sharp spikes. Also for automatically detected HFOs, surgical outcome is better when locations with higher HFO rates are included in the resection. We hypothesized that high frequency oscillations (HFOs) with irregular amplitude and frequency more specifically reflect epileptogenicity than HFOs with stable amplitude and frequency. Methods:We developed a fully automatic algorithm to detect HFOs and classify them based on their morphology, with types defined according to regularity in amplitude and frequency: type 1 with regular amplitude and frequency; type 2 with irregular amplitude, which could result from filtering of sharp spikes; type 3 with irregular frequency; and type 4 with irregular amplitude and frequency. We investigated the association of different HFO types with the seizure onset zone (SOZ), resected area and surgical outcome. Results:HFO rates of all types were significantly higher inside the SOZ than outside. HFO types 1 and 2 were strongly correlated to each other and showed the highest rates among all HFOs. Their occurrence was highly associated with the SOZ, resected area and surgical outcome. The automatic detection emulated visual markings with 93% true positives and 57% false detections. Conclusions:HFO types 1 and 2 similarly reflect epileptogenicity. Significance:For clinical application, it may not be necessary to separate real HFOs from "false oscillations" produced by the filter effect of sharp spikes. Also for automatically detected HFOs, surgical outcome is better when locations with higher HFO rates are included in the resection.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

“…In accord with other published literature (Burnos et al, 2016), both true and false ripples on spike rates were equivalently increased in the SOZ as compared with the NSOZ.…”

A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings

“…Though it remains uncertain that true HFOs exhibit superior accuracy for delineating epileptogenic brain regions, as compared with sharply contoured IES events (Burnos et al, 2016), the subject merits further investigation. Furthermore, only true ripple on spike events can be assigned properties such as mean spectral content, power, and duration.…”

“…Utilization of this method will assist in the determination of whether true HFO events are generated by distinct mechanisms as compared with false HFO events. Further, it will aid in the investigation of true and false RonS with respect to understanding and differentiating their mechanisms of generation (Schevon et al, 2009; Keller et al, 2010), utility for identifying epileptogenic regions (Burnos et al, 2016), role during seizures (Eissa et al, 2016), and also their ability to disrupt normal cognition (Horak et al, 2017). The topographical method we describe is one of many “computer vision” based approaches to identifying multiple distinct brief oscillatory events that overlap in time, but are unique in spectral content (Burnos et al, 2014; Kucewicz et al, 2017).…”

Advances of Intracranial Electroencephalography in Localizing the Epileptogenic Zone

“…The morphology of HFOs does not improve delineation of the EZ either [56]. Analyzing the EEG characteristics associated with ripples may be useful for identifying pathological ripples.…”