Exclusion of the Possibility of “False Ripples” From Ripple Band High-Frequency Oscillations Recorded From Scalp Electroencephalogram in Children With Epilepsy

Abstract

AimRipple-band epileptic high-frequency oscillations (HFOs) can be recorded by scalp electroencephalography (EEG), and tend to be associated with epileptic spikes. However, there is a concern that the filtration of steep waveforms such as spikes may cause spurious oscillations or “false ripples.” We excluded such possibility from at least some ripples by EEG differentiation, which, in theory, enhances high-frequency signals and does not generate spurious oscillations or ringing.MethodsThe subjects were 50 pediatric patients, and ten consecutive spikes during sleep were selected for each patient. Five hundred spike data segments were initially reviewed by two experienced electroencephalographers using consensus to identify the presence or absence of ripples in the ordinary filtered EEG and an associated spectral blob in time-frequency analysis (Session A). These EEG data were subjected to numerical differentiation (the second derivative was denoted as EEG″). The EEG″ trace of each spike data segment was shown to two other electroencephalographers who judged independently whether there were clear ripple oscillations or uncertain ripple oscillations or an absence of oscillations (Session B).ResultsIn Session A, ripples were identified in 57 spike data segments (Group A-R), but not in the other 443 data segments (Group A-N). In Session B, both reviewers identified clear ripples (strict criterion) in 11 spike data segments, all of which were in Group A-R (p < 0.0001 by Fisher’s exact test). When the extended criterion that included clear and/or uncertain ripples was used in Session B, both reviewers identified 25 spike data segments that fulfilled the criterion: 24 of these were in Group A-R (p < 0.0001).DiscussionWe have demonstrated that real ripples over scalp spikes exist in a certain proportion of patients. Ripples that were visualized consistently using both ordinary filters and the EEG″ method should be true, but failure to clarify ripples using the EEG″ method does not mean that true ripples are absent.ConclusionThe numerical differentiation of EEG data provides convincing evidence that HFOs were detected in terms of the presence of such unusually fast oscillations over the scalp and the importance of this electrophysiological phenomenon.

Scalp HFO rates are higher for larger lesions

“…Finally, we calculated a z‐score from the 250–500 Hz band‐pass‐filtered data overtime and rejected events occurring at high z‐score timepoints, exceeding the median z‐score by 1.5 times the interquartile range for each patient 10 . Although our automated detector uses a finite impulse response (FIR) filter that is expected to reduce induced oscillations, 28 this study did not further distinguish between false ripples and ripples observable without a high‐pass filter 29,30 . We calculated the HFO rate for each bipolar channel by dividing the number of detected HFO by the duration of the analyzed EEG, resulting in the unit HFO/min.…”

“…10 Although our automated detector uses a finite impulse response (FIR) filter that is expected to reduce induced oscillations, 28 this study did not further distinguish between false ripples and ripples observable without a high-pass filter. 29,30 We calculated the HFO rate for each bipolar channel by dividing the number of detected HFO by the duration of the analyzed EEG, resulting in the unit HFO/min. We identified the HFO area as an area delineated by bipolar channels with consistently high HFO rates across scalp EEG channels for each patient that were defined as HFO area channels.…”

Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples

“…While in some other brain cases, for instance, cognitive task, vision, movement, epilepsy, etc., high frequency true ripples are used as a biomarker [ 42 , 43 , 44 , 45 ]. Therefore, in these cases, removing spikes and sharp artifacts, which cause false ripples, is required [ 46 , 47 ]. In fact, initially detecting true ripples using any algorithm required a band-pass filter; therefore, any sharp signal passing through this filter would be presented as a false ripple which is due to the ringing effect of this filter.…”

“…In fact, initially detecting true ripples using any algorithm required a band-pass filter; therefore, any sharp signal passing through this filter would be presented as a false ripple which is due to the ringing effect of this filter. As a result, these false ripples would affect the outcomes and cause medical misrepresentation [ 46 ].…”