Frequency-independent characteristics of high-frequency oscillations in epileptic and non-epileptic regions

Objective

The purpose of the presented study is to determine whether there are frequency-independent high-frequency oscillation (HFO) parameters which may differ in epileptic and non-epileptic regions.

Clinical Utility of Interictal High-Frequency Oscillations Recorded with Subdural Macroelectrodes in Partial Epilepsy

Background and Purpose

There is growing interest in high-frequency oscillations (HFO) as electrophysiological biomarkers of the epileptic brain. We evaluated the clinical utility of interictal HFO events, especially their occurrence rates, by comparing the spatial distribution with a clinically determined epileptogenic zone by using subdural macroelectrodes.

Methods

We obtained intracranial electroencephalogram data with a high temporal resolution (2000 Hz sampling rate, 0.05-500 Hz band-pass filter) from seven patients with medically refractory epilepsy. Three epochs of 5-minute, artifact-free data were selected randomly from the interictal period. HFO candidates were first detected by an automated algorithm and subsequently screened to discard false detections. Validated events were further categorized as fast ripple (FR) and ripple (R) according to their spectral profiles. The occurrence rate of HFOs was calculated for each electrode contact. An HFO events distribution map (EDM) was constructed for each patient to allow visualization of the spatial distribution of their HFO events.

Results

The subdural macroelectrodes were capable of detecting both R and FR events from the epileptic neocortex. The occurrence rate of HFO events, both FR and R, was significantly higher in the seizure onset zone (SOZ) than in other brain regions. Patient-specific HFO EDMs can facilitate the identification of the location of HFO-generating tissue, and comparison with findings from ictal recordings can provide additional useful information regarding the epileptogenic zone.

Conclusions

The distribution of interictal HFOs was reasonably consistent with the SOZ. The detection of HFO events and construction of spatial distribution maps appears to be useful for the presurgical mapping of the epileptogenic zone.     

Interictal high frequency oscillations (HFOs) in patients with focal epilepsy and normal MRI

Objective

We aim to analysis the relationship between HFOs-generating regions and the seizure onset zone (SOZ) in epileptic patients without a visible lesion on MRI.

Methods

Intracerebral EEGs were recorded in 17 patients with intractable focal seizures and normal MRIs. The rates of interictal HFOs and spikes inside and outside the SOZ were analyzed as well as the specificity, sensitivity and accuracy of HFOs and spikes to determine the SOZ.

Results

The mean rate of spikes, ripples and fast ripples (FR) was higher in the SOZ than in the non-SOZ channels. In regard to the identification of the SOZ the sensitivity was 91% for spikes, 91% for ripples and 66% for FR, the specificity was 30% for spikes, 42% for ripples and 80% for FR, and the accuracy was 44% for spikes, 54% for ripples and 76% for FR.

Conclusions

The rates of spikes and HFOs were higher inside than outside the SOZ. However, HFOs are also more specific and accurate than spikes to delineate the SOZ.

Significance

Analysis of interictal HFOs during 5-10 min of sleep recording is a good tool to localize the SOZ in patients with epilepsy and normal MRI, and could potentially reduce the duration of chronic intracerebral EEG recordings.     

Detection of recurrent activation patterns across focal seizures: Application to seizure onset zone identification

Objective

We introduce a method that quantifies the consistent involvement of intracranially monitored regions in recurrent focal seizures.

Interictal network properties in mesial temporal lobe epilepsy: A graph theoretical study from intracerebral recordings

Objective

Graph theoretical analysis of functional connectivity data has demonstrated a small-world topology of brain networks. There is increasing evidence that the topology of brain networks is changed in epilepsy. Here we investigated the basal properties of epileptogenic networks by applying graph analysis to intracerebral EEG recordings of patients presenting with drug-resistant partial epilepsies during the interictal period.

Methods

Interictal EEG activity was recorded in mesial temporal lobe of 11 patients with mesial temporal lobe epilepsy (MTLE group) and compared with a “control” group of 8 patients having neocortical epilepsies (non MTLE group) in whom depth-EEG recordings eventually showed an ictal onset outside the MTL structures. Synchronization likelihood (SL) was calculated between selected intracerebral electrodes contacts to obtain SL-weighted graphs. Mean normalized clustering index, average path length and small world index S were calculated to characterize network organization.

Results

Broadband SL values were higher in the MTLE group. Although a small-world pattern was found in the two groups, normalized clustering index and to a lesser extend average path length were higher in the MTLE group.

Conclusions

We demonstrated a trend toward a more regular (less random) configuration of interictal epileptogenic networks. In addition S index was found to correlate with epilepsy duration.

Significance

These topological alterations might be a surrogate marker of human focal epilepsy and disclose some changes over time.     

Intracranially recorded interictal spikes: Relation to seizure onset area and effect of medication and time of day

Objective

The relationship between seizures and interictal spikes remains undetermined. We analyzed intracranial EEG (icEEG) recordings to examine the relationship between the seizure onset area and interictal spikes.

Methods

80 unselected patients were placed into 5 temporal, 4 extratemporal, and one unlocalized groups based on the location of the seizure onset area. We studied 4-h icEEG epochs, removed from seizures, from day-time and night-time during both on- and off-medication periods. Spikes were detected automatically from electrode contacts sampling the hemisphere ipsilateral to the seizure onset area.

Results

There was a widespread occurrence of spikes over the hemisphere ipsilateral to the seizure onset area. The spatial distributions of spike rates for the different patient groups were different (p < 0.0001, chi-square test). The area with the highest spike rate coincided with the seizure onset area only in half of the patients.

Conclusion

The spatial distribution of spike rates is strongly associated with the location of the seizure onset area, suggesting the presence of a distributed spike generation network, which is related to the seizure onset area.

Significance

The spatial distribution of spike rates, but not the area with the highest spike rate, may hold value for the localization of the seizure onset area.     

Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography

Objective

To test the utility of a novel semi-automated method for detecting, validating, and quantifying high-frequency oscillations (HFOs): ripples (80–200?Hz) and fast ripples (200–600?Hz) in intra-operative electrocorticography (ECoG) recordings.

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

Objective

To develop and validate a detector that identifies ripple (80–200?Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ).

Effect of sleep stage on interictal high-frequency oscillations recorded from depth macroelectrodes in patients with focal epilepsy

Purpose :  To investigate the effect of sleep stage on the properties of high-frequency oscillations (HFOs) recorded from depth macroelectrodes in patients with focal epilepsy.
Methods :  Ten-minute epochs of wakefulness (W), stage 1–2 non-REM (N1-N2), stage 3 non-REM (N3) and REM sleep (R) were identified from stereo-electroencephalography (SEEG) data recorded at 2 kHz in nine patients. Rates of spikes, ripples (>80 Hz), and fast ripples (>250 Hz) were calculated, as were HFO durations, degree of spike–HFO overlap, HFO rates inside and outside of spikes, and inside and outside of the seizure-onset zone (SOZ).
Results :  Ripples were observed in nine patients and fast ripples in eight. Spike rate was highest in N1-N2 in 5 of 9 patients, and in N3 in 4 of 9 patients, whereas ripple rate was highest in N1-N2 in 4 of 9 patients, in N3 in 4 of 9 patients, and in W in 1 of 9 patients. Fast ripple rate was highest in N1-N2 in 4 of 8 patients, and in N3 in 4 of 8 patients. HFO properties changed significantly with sleep stage, although the absolute effects were small. The difference in HFO rates inside and outside of the SOZ was highly significant (p < 0.000001) in all stages except for R and, for fast ripples, only marginally significant (p = 0.018) in W.
Conclusions :  Rates of HFOs recorded from depth macroelectrodes are highest in non-REM sleep. HFO properties were similar in stages N1-N2 and N3, suggesting that accurate sleep staging is not necessary. The spatial specificity of HFO, particularly fast ripples, was affected by sleep stage, suggesting that recordings excluding REM sleep and wakefulness provide a more reliable indicator of the SOZ.     

Interictal ripples nested in epileptiform discharge help to identify the epileptogenic zone in neocortical epilepsy

Objective

This study aimed to identify the subtype of interictal ripples that help delineate the epileptogenic zone in neocortical epilepsy.

Visual and semi-automatic non-invasive detection of interictal fast ripples: A potential biomarker of epilepsy in children with tuberous sclerosis complex

Objectives

We aim to establish that interictal fast ripples (FR; 250–500?Hz) are detectable on scalp EEG, and to investigate their association to epilepsy.

Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram

Objective

Fast ripples (FR, 250–500 Hz) in the intraoperative corticogram have recently been proposed as specific predictors of surgical outcome in epilepsy patients. However, online FR detection is restricted by their low signal-to-noise ratio. Here we propose the integration of low-noise EEG with unsupervised FR detection.

Delayed high-frequency suppression after automated single-pulse electrical stimulation identifies the seizure onset zone in patients with refractory epilepsy

Objective

Single-pulse electrical stimulation (SPES) of intracranial electrodes evokes responses that may help identify the seizure onset zone (SOZ); however, lack of automation and response variability has limited clinical adoption of this technique. We evaluated whether automated delivery of low-current SPES could evoke delayed high-frequency suppression (DHFS) of ongoing electrocorticography (ECoG) signals that, when combined with objective analytic techniques, may provide a reliable marker of this zone.

Ripple classification helps to localize the seizure‐onset zone in neocortical epilepsy

Purpose: Fast ripples are reported to be highly localizing to the epileptogenic or seizure‐onset zone (SOZ) but may not be readily found in neocortical epilepsy, whereas ripples are insufficiently localizing. Herein we classified interictal neocortical ripples by associated characteristics to identify a subtype that may help to localize the SOZ in neocortical epilepsy. We hypothesize that ripples associated with an interictal epileptiform discharge (IED) are more pathologic, since the IED is not a normal physiologic event. Methods: We studied 35 patients with epilepsy with neocortical epilepsy who underwent invasive electroencephalography (EEG) evaluation by stereotactic EEG (SEEG) or subdural grid electrodes. Interictal fast ripples and ripples were visually marked during slow‐wave sleep lasting 10–30 min. Neocortical ripples were classified as type I when superimposed on epileptiform discharges such as paroxysmal fast, spike, or sharp wave, and as type II when independent of epileptiform discharges. Key Findings: In 21 patients with a defined SOZ, neocortical fast ripples were detected in the SOZ of only four patients. Type I ripples were detected in 14 cases almost exclusively in the SOZ or primary propagation area (PP) and marked the SOZ with higher specificity than interictal spikes. In contrast, type II ripples were not correlated with the SOZ. In 14 patients with two or more presumed SOZs or nonlocalizable onset pattern, type I but not type II ripples also occurred in the SOZs. We found the areas with only type II ripples outside of the SOZ (type II‐O ripples) in SEEG that localized to the primary motor cortex and primary visual cortex. Significance: Neocortical fast ripples and type I ripples are specific markers of the SOZ, whereas type II ripples are not. Type I ripples are found more readily than fast ripples in human neocortical epilepsy. Type II‐O ripples may represent spontaneous physiologic ripples in the human neocortex.     

Is seizure detection based on EKG clinically relevant?

Objective

Real-time EKG-based automated seizure detection is emerging as a complement or supplement to that based on cortical signals, but its value is unproven. This study assesses the clinically relevance of EKG-based seizure detection by comparing the information content in EKG and ECoG.

Methods

ECoGs (6935 h; 241 clinical and 4311 sub-clinical seizures) with simultaneous EKG from 81 subjects undergoing surgical evaluation were used in these analyses. Differences, if any, between clinical and sub-clinical seizures in variables such as intensity, duration and their product severity, were investigated with a multi-variate regression model.

Results

Highly statistically significant differences in severity between clinical and sub-clinical seizures were discerned with EKG and ECoG. Furthermore, EKG-based seizure severity was linearly correlated with that estimated using ECoG.

Conclusions

These findings support the notion that EKG-based seizure detection is clinically relevant in certain localization-related epilepsies, providing similar information to that yielded by neuronal electrical signals.

Significance

The information content equivalence between EKG and ECoG would enable automated seizure detection, quantification and therapy delivery, without resorting to cortical monitoring. The considerably higher S/N and ease of acquisition and processing of EKG compared to ECoG/EEG may foster widespread clinical applications of this novel detection approach.     

Frequency–moment signatures: A method for automated seizure detection from scalp EEG

Objectives

To investigate patient-specific automated epileptic seizure detection from scalp EEG using a new technique: frequency–moment signatures.

Methods

Signatures were calculated from 32 s blocks of data of electrode differences from the right (RH) and left hemisphere (LH). Discrete Fourier transforms of 15 data subsets were calculated per block per hemisphere. The spectral powers at a given frequency from the RH and LH were combined into a single quantity. The signature elements were found by subtracting normalised central moments of the subset distribution from the mean, to measure the consistency of the spectral power at a given frequency over all subsets. The seizure measure was the logarithm of the probability that a signature belonged to a control set of non-seizure signatures.

Results

Following the optimisation of signature parameters using three one-day recordings from each of 12 patients, performance was tested on a separate set of data from the same patients. The method had a sensitivity of 91.0% (total 34 seizures) with 0.020 false positives per hour (total 618 h).

Conclusions

Frequency–moment signatures promise automated seizure detection sensitivities comparable to visual identification and other published methods, with improved false detection rates.

Significance

This technique has the potential to be used more widely in EEG analysis.     

Modeling of intracerebral interictal epileptic discharges: Evidence for network interactions

Objective

The interictal epileptic discharges (IEDs) occurring in stereotactic EEG (SEEG) recordings are in general abundant compared to ictal discharges, but difficult to interpret due to complex underlying network interactions. A framework is developed to model these network interactions.

Dynamic imaging of seizure activity in pediatric epilepsy patients

Objective

To investigate the feasibility of using noninvasive EEG source imaging approach to image continuous seizure activity in pediatric epilepsy patients.

Methods

Nine pediatric patients with medically intractable epilepsy were included in this study. Eight of the patients had extratemporal lobe epilepsy and one had temporal lobe epilepsy. All of the patients underwent resective surgery and seven of them underwent intracranial EEG (iEEG) monitoring. The ictal EEG was analyzed using a noninvasive dynamic seizure imaging (DSI) approach. The DSI approach separates scalp EEGs into independent components and extracts the spatio-temporal ictal features to achieve dynamic imaging of seizure sources. Surgical resection and intracranial recordings were used to validate the noninvasive imaging results.

Results

The DSI determined seizure onset zones (SOZs) in these patients were localized within or in close vicinity to the surgically resected region. In the seven patients with intracranial monitoring, the estimated seizure onset sources were concordant with the seizure onset zones of iEEG. The DSI also localized the multiple foci involved in the later seizure propagation, which were confirmed by the iEEG recordings.

Conclusions

Dynamic seizure imaging can noninvasively image the seizure activations in pediatric patients with both temporal and extratemporal lobe epilepsy.

Significance

EEG seizure imaging can potentially be used to noninvasively image the SOZs and aid the pre-surgical planning in pediatric epilepsy patients.     

Very high frequency oscillations (over 1000 Hz) in human epilepsy

Objective

High frequency oscillations (HFO) of 100–500 Hz have been reported in epileptic human brain. However, the questions of how fast these oscillations can reach, and which frequency range is clinically important remain unanswered. We recorded interictal and ictal very high frequency oscillations (VHFO) of 1000–2500 Hz by subdural electrodes using 10 kHz sampling rate. We describe the characteristics of VHFO, and discuss their underlying mechanism and clinical significance.

Methods

Five patients with neocortical epilepsy were studied. All patients underwent intracranial EEG monitoring with subdural electrodes. EEG recording with sampling rate of 10 kHz was conducted. Histopathology revealed malformation of cortical development in all cases.

Results

In four of five patients, very high frequency activities of 1000–2500 Hz were detected in highly localized cortical regions (one to four electrodes in individual patient). We named these activities “very high frequency oscillations (VHFO)”. Interictally, VHFO appeared intermittently, and were interrupted by spikes. Sustained VHFO without spikes appeared around the start of seizures.

Conclusions

Both interictal and ictal VHFO can be recorded by subdural electrodes. Compared to HFO previously reported, VHFO have much higher frequency, more restricted distribution, smaller amplitude, and different timing of onset.

Significance

Recording of VHFO may be useful for identifying the epileptogenic zone.     

Intraoperative subdural low-noise EEG recording of the high frequency oscillation in the somatosensory evoked potential

Objective

The detectability of high frequency oscillations (HFO, >200 Hz) in the intraoperative ECoG is restricted by their low signal-to-noise ratio (SNR). Using the somatosensory evoked HFO, we quantify how HFO detectability can benefit from a custom-made low-noise amplifier (LNA).