Interictal Epileptiform Spikes Research Articles

Deep brain stimulation of thalamic nucleus reuniens promotes neuronal and cognitive resilience in an Alzheimer’s disease mouse model

The mechanisms that confer cognitive resilience to Alzheimer’s Disease (AD) are not fully understood. Here, we describe a neural circuit mechanism underlying this resilience in a familial AD mouse model. In the prodromal disease stage, interictal epileptiform spikes (IESs) emerge during anesthesia in the CA1 and mPFC regions, leading to working memory disruptions. These IESs are driven by inputs from the thalamic nucleus reuniens (nRE). Indeed, tonic deep brain stimulation of the nRE (tDBS-nRE) effectively suppresses IESs and restores firing rate homeostasis under anesthesia, preventing further impairments in nRE-CA1 synaptic facilitation and working memory. Notably, applying tDBS-nRE during the prodromal phase in young APP/PS1 mice mitigates age-dependent memory decline. The IES rate during anesthesia in young APP/PS1 mice correlates with later working memory impairments. These findings highlight the nRE as a central hub of functional resilience and underscore the clinical promise of DBS in conferring resilience to AD pathology by restoring circuit-level homeostasis.

Resting-state background features demonstrate multidien cycles in long-term EEG device recordings

BackgroundLongitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. Objective/hypothesisWe hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may temporarily interrupt these cycles. MethodsWe analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. ResultsBackground EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56–0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53–0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55–0.64]). ConclusionsOur findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may temporarily interrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.

Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation.

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

Multiview Feature Fusion Representation for Interictal Epileptiform Spikes Detection.

Interictal epileptiform spikes (IES) of scalp electroencephalogram (EEG) signals have a strong relation with the epileptogenic region. Since IES are highly unlikely to be detected in scalp EEG signals, the primary diagnosis depends heavily on the visual evaluation of IES. However, visual inspection of EEG signals, the standard IES detection procedure is time-consuming, highly subjective, and error-prone. Furthermore, the highly complex, nonlinear, and nonstationary characteristics of EEG signals lead to the incomplete representation of EEG signals in existing computer-aided methods and consequently unsatisfactory detection performance. Therefore, a novel multiview feature fusion representation (MVFFR) method was developed and combined with a robustness classifier to detect EEG signals with/without IES. MVFFR comprises two steps: First, temporal, frequency, temporal-frequency, spatial, and nonlinear domain features are transformed by the IES to express the latent information effectively. Second, the unsupervised infinite feature-selection method determines the most distinct feature fusion representations. Experimental results using a balanced dataset of six patients showed that MVFFR achieved the optimal detection performance (accuracy: 89.27%, sensitivity: 89.01%, specificity: 89.54%, and precision: 89.82%) compared with other feature ranking methods, and the MVFFR-related method were complementary and indispensable. Additionally, in an independent test, MVFFR maintained excellent generalization capacity with a false detection rate per minute of 0.15 on the unbalanced dataset of one patient.

Multilevel Feature Learning Method for Accurate Interictal Epileptiform Spike Detection.

Interictal epileptiform spike (referred to as spike) detected from electroencephalograms lasting only 20- to 200-ms can provide a reliable evidence-based indicator for clinical seizure type diagnosis. Recent feature representation approaches focus either on the concrete-level or abstract-level information mining of the spike, thus demonstrating suboptimal detection performance. Additionally, existing abstract-level information mining methods of the spike based deep learning networks have not realized the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity, which affects detection performance. Thus, a multilevel feature learning method for accurate spike detection was proposed in this study. Specifically, the spatio-temporal-frequency multidomain information in concrete-level first are inferred the common mimetic properties of the spike using the multidomain feature extractors. Then, the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity is suitably captured using the temporal convolutional network. Finally, the spatio-temporal-frequency multidomain long-term dependent feature representation of spike is calculated using the element-wise manner to fuse the feature representation in concrete- and abstract-levels. The experimental results indicate that the proposed method can achieve an accuracy of 90.62±1.38%, sensitivity of 90.38±1.52%, specificity of 91.00±1.60%, precision of 90.33±4.71%, and the false detection rate per minute is [Formula: see text], which are higher than when using the feature representation in the concrete-or abstract-level alone. Additionally, the detection results indicate that the proposed method avoids the subjectivity and inefficiency of visual inspection, and it enables a highly accurate detection of the spike.

Thalamic deep brain stimulation modulates cycles of seizure risk in epilepsy

Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months. All subjects had circadian and multiday cycles in the rate of interictal epileptiform spikes (IES). There was seizure phase locking to circadian and multiday IES cycles in five and seven out of eight subjects, respectively. Thalamic DBS modified circadian (all 3 subjects) and multiday (analysis limited to the human participant) IES cycles. DBS modified seizure clustering and circadian phase locking in the human subject. Multiscale cycles in brain excitability and seizure risk are features of human and canine epilepsy and are modifiable by thalamic DBS.

Leveraging electrophysiologic correlates of word encoding to map seizure onset zone in focal epilepsy: Task-dependent changes in epileptiform activity, spectral features, and functional connectivity.

Verbal memory dysfunction is common in focal, drug-resistant epilepsy (DRE). Unfortunately, surgical removal of seizure-generating brain tissue can be associated with further memory decline. Therefore, localization of both the circuits generating seizures and those underlying cognitive functions is critical in presurgical evaluations for patients who may be candidates for resective surgery. We used intracranial electroencephalographic (iEEG) recordings during a verbal memory task to investigate word encoding in focal epilepsy. We hypothesized that engagement in a memory task would exaggerate local iEEG feature differences between the seizure onset zone (SOZ) and neighboring tissue as compared to wakeful rest ("nontask"). Ten participants undergoing presurgical iEEG evaluation for DRE performed a free recall verbal memory task. We evaluated three iEEG features in SOZ and non-SOZ electrodes during successful word encoding and compared them with nontask recordings: interictal epileptiform spike (IES) rates, power in band (PIB), and relative entropy (REN; a functional connectivity measure). We found a complex pattern of PIB and REN changes in SOZ and non-SOZ electrodes during successful word encoding compared to nontask. Successful word encoding was associated with a reduction in local electrographic functional connectivity (increased REN), which was most exaggerated in temporal lobe SOZ. The IES rates were reduced during task, but only in the non-SOZ electrodes. Compared with nontask, REN features during task yielded marginal improvements in SOZ classification. Previous studies have supported REN as a biomarker for epileptic brain. We show that REN differences between SOZ and non-SOZ are enhanced during a verbal memory task. We also show that IESs are reduced during task in non-SOZ, but not in SOZ. These findings support the hypothesis that SOZ and non-SOZ respond differently to task and warrant further exploration into the use of cognitive tasks to identify functioning memory circuits and localize SOZ.

Altered Sleep-Related Consolidation and Neurocognitive Comorbidity in CECTS.

Our aim is to use neurophysiological sleep-related consolidation (SRC) phenomena to identify putative pathophysiological mechanisms in CECTS linked to diffuse neurocognitive deficits. We argue that there are numerous studies on the association between seizure aspects and neurocognitive functioning but not as many on interictal variables and neurocognitive deficits. We suggest two additional foci. First, the interictal presentation in CECTS and second, neuronal oscillations involved in SRC processes. Existing data on mechanisms through which interictal epileptiform spikes (IES) impact upon SRC indicate that they have the potential to: (a) perturb cross-regional coupling of neuronal oscillations, (b) mimic consolidation processes, (c) alter the precision of the spatiotemporal coupling of oscillations, and (d) variably impact upon SRC performance. Sleep spindles merit systematic study in CECTS in order to clarify: (a) the state of the slow oscillations (SOs) with which they coordinate, (b) the precision of slow oscillation-spindle coupling, and (c) whether their developmental trajectories differ from those of healthy children. We subsequently review studies on the associations between IES load during NREM sleep and SRC performance in childhood epilepsy. We then use sleep consolidation neurophysiological processes and their interplay with IES to help clarify the diffuse neurocognitive deficits that have been empirically documented in CECTS. We claim that studying SRC in CECTS will help to clarify pathophysiological mechanisms toward diverse neurocognitive deficits. Future developments could include close links between the fields of epilepsy and sleep, as well as new therapeutic neurostimulation targets. At the clinical level, children diagnosed with CECTS could benefit from close monitoring with respect to epilepsy, sleep and neurocognitive functions.

Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we present a new approach based on convolutional neural network (CNN) to detect IEDs from EEGs automatically. The input to CNN is a combination of raw EEG and frequency sub-bands, namely delta, theta, alpha and, beta arranged as a vector for one-dimensional (1D) CNN or matrix for two-dimensional (2D) CNN. The proposed method is evaluated on 554 scalp EEGs. The database consists of 18,164 IEDs marked by two neurologists. Five-fold cross-validation was performed to assess the IED detectors. The resulting 1D CNN based IED detector with multiple sub-bands achieved a false positive rate per minute of 0.23 and a precision of 0.79 at 90% sensitivity. Further, the proposed system is evaluated on datasets from three other clinics, and the features extracted from CNN outputs could significantly discriminate (p-values <; 0.05) the EEGs with and without IEDs. We have proposed an optimized method with better performance than the literature that could aid clinicians to diagnose epilepsy expeditiously, and thereby devise proper treatment.

KCC2 antagonism and gabaergic synchronization in the entorhinal cortex in the absence of ionotropic glutamatergic receptor signalling

γ-Aminobutyric acid (GABA), which is released by interneurons, plays an active role in generating interictal epileptiform spikes during blockade of ionotropic glutamatergic signalling, but it remains unclear whether and how the K+-Cl− cotransporter 2 (KCC2) influences these paroxysmal events. Therefore, we employed tetrode recordings in the in vitro rat entorhinal cortex (EC) to analyze the effects of the KCC2 antagonist VU0463271 on 4-aminopyridine (4AP)-induced interictal spikes that were pharmacologically isolated by applying ionotropic glutamatergic receptor antagonists. After the addition of VU0463271, these interictal spikes continued to occur at similar rates as in control (i.e., during application of 4AP with ionotropic glutamatergic receptor antagonists) but were smaller and shorter. Despite the absence of ionotropic glutamatergic receptor signalling, both interneurons and principal cells increased their firing during interictal spikes. Moreover, we found that KCC2 antagonism increased interneuron firing but decreased principal cell firing during the interictal spike rising phase; in contrast, during the falling phase, interneuron firing decreased in the presence of VU0463271 while no change was observed in principal cell firing. Overall, our results show that KCC2 antagonism enhances interneuron excitability at the onset of interictal spikes generated by the EC neuronal networks during blockade of ionotropic glutamatergic transmission but disrupts later neuronal recruitment.

Improved identification and differentiation from epileptiform activity of human hippocampal sharp wave ripples during NREM sleep.

In rodents, pyramidal cell firing patterns from waking may be replayed in nonrapid eye movement sleep (NREM) sleep during hippocampal sharp wave ripples (HC-SWR). In humans, HC-SWR have only been recorded with electrodes implanted to localize epileptogenicity. Here, we characterize human HC-SWR with rigorous rejection of epileptiform activity, requiring multiple oscillations and coordinated sharp waves. We demonstrated typical SWR in those rare HC recordings which lack interictal epileptiform spikes (IIS) and with no or minimal seizure involvement. These HC-SWR have a similar rate (~12 min-1 on average, variable across NREM stages and anterior/posterior HC) and apparent intra-HC topography (ripple maximum in putative stratum pyramidale, slow wave in radiatum) as rodents, though with lower frequency (~85 Hz compared to ~140 Hz in rodents). Similar SWR are found in HC with IIS, but no significant seizure involvement. These SWR were modulated by behavior, being largely absent (<2 min-1 ) except during NREM sleep in both Stage 2 (~9 min-1 ) and Stage 3 (~15 min-1 ), distinguishing them from IIS. This study quantifies the basic characteristics of a strictly selected sample of SWR recorded in relatively healthy human hippocampi.

MicroRNA inhibition upregulates hippocampal A-type potassium current and reduces seizure frequency in a mouse model of epilepsy

Epilepsy is often associated with altered expression or function of ion channels. One example of such a channelopathy is the reduction of A-type potassium currents in the hippocampal CA1 region. The underlying mechanisms of reduced A-type channel function in epilepsy are unclear. Here, we show that inhibiting a single microRNA, miR-324-5p, which targets the pore-forming A-type potassium channel subunit Kv4.2, selectively increased A-type potassium currents in hippocampal CA1 pyramidal neurons in mice. Resting membrane potential, input resistance and other potassium currents were not altered. In a mouse model of acquired chronic epilepsy, inhibition of miR-324-5p reduced the frequency of spontaneous seizures and interictal epileptiform spikes supporting the physiological relevance of miR-324-5p-mediated control of A-type currents in regulating neuronal excitability. Mechanistic analyses demonstrated that microRNA-induced silencing of Kv4.2 mRNA is increased in epileptic mice leading to reduced Kv4.2 protein levels, which is mitigated by miR-324-5p inhibition. By contrast, other targets of miR-324-5p were unchanged. These results suggest a selective miR-324-5p-dependent mechanism in epilepsy regulating potassium channel function, hyperexcitability and seizures.

Detection of Spikes With Defined Parameters in the ECoG Signal

Patients undergoing presurgical evaluation for resective epilepsy surgery require the localization of the epileptogenic zone. The detection and analysis of interictal and ictal epileptiform spikes are of major importance for identifying this area. The “irritative zone” of the cortex with interictal spikes is usually revealed intraoperatively during acute electrocorticography (ECoG). Since ECoG recordings cannot be completely visually reviewed in a reasonable amount of time, computer algorithms for the automatic detection of seizures and spikes were developed. We proposed the spike detection algorithm based on the definition of the physical properties of the sought spikes (duration and amplitude). The parameters are easily interpreted by neurophysiologists. The detection sensitivity was 93%, and the precision was 99%. In addition, based on the number of spikes detected on individual electrodes, it is possible to draw the “frequency of spike occurrence” map. The algorithm proved to be so effective in analyzing ECoG records, as it is allowed to be used in practice.

Physiological Ripples (\xb1\u2009100\xa0Hz) in Spike-Free Scalp EEGs of Children With and Without Epilepsy

Pathological high frequency oscillations (HFOs, >80 Hz) are considered new biomarkers for epilepsy. They have mostly been recorded invasively, but pathological ripples (80–250 Hz) can also be found in scalp EEGs with frequent epileptiform spikes. Physiological HFOs also exist. They have been recorded invasively in hippocampus and neocortex. There are no reports of spontaneously occurring physiological HFOs recorded with scalp EEG. We aimed to study ripples in spike-free scalp EEGs. We included 23 children (6 with, 17 without epilepsy) who had an EEG without interictal epileptiform spikes recorded during sleep. We differentiated true ripples from spurious ripples such as filtering effects of sharp artifacts and high frequency components of muscle artifacts by viewing ripples simultaneously in bipolar and average montage and double-checking the unfiltered signal. We calculated mean frequency, duration and root mean square amplitude of the ripples, and studied their shape and distribution. We found ripples in EEGs of 20 out of 23 children (4 with, 16 without epilepsy). Ripples had a regular shape and occurred mostly on central and midline channels. Mean frequency was 102 Hz, mean duration 70 ms, mean root mean square amplitude 0.95 µV. Ripples occurring in normal EEGs of children without epilepsy were considered physiological; the similarity in appearance suggested that the ripples occurring in normal EEGs of children with epilepsy were also physiological. The finding that it is possible to study physiological neocortical ripples in scalp EEG paves the way for investigating their occurrence during brain development and their relation with cognitive functioning.

Automated epileptiform spike detection via affinity propagation-based template matching.

Interictal epileptiform spikes are the key diagnostic biomarkers for epilepsy. The clinical gold standard of spike detection is visual inspection performed by neurologists. This is a tedious, time-consuming, and expert-centered process. The development of automated spike detection systems is necessary in order to provide a faster and more reliable diagnosis of epilepsy. In this paper, we propose an efficient template matching spike detector based on a combination of spike and background waveform templates. We generate a template library by clustering a collection of spikes and background waveforms extracted from a database of 50 patients with epilepsy. We benchmark the performance of five clustering techniques based on the receiver operating characteristic (ROC) curves. In addition, background templates are integrated with existing spike templates to improve the overall performance. The affinity propagation-based template matching system with a combination of spike and background templates is shown to outperform the other four conventional methods with the highest area-under-curve (AUC) of 0.953.

P89. Differential diagnosis between epilepsy and parasomnia may be difficult even on 10–20 video-EEG

Sleep related attacks of bizarre complex movements may be due to either epilepsy or non-REM parasomnia. Differential diagnosis can be especially difficult in mentally retarded patients due to lack of detailed medical history and presence of epilepsy risk factors such as brain lesions. We present the case of a 19 year-old male with history of preterm birth, perinatal intracerebral hemorrhage, and post-hemorrhagic hydrocephalus. His parents reported up to 20 attacks per night characterized by startle from sleep, fearful expression in his eyes, grabbing hold of the bedframe, body rocking, and groaning, each episode lasting between 10 and 30 s. Several standard EEGs were normal. Cranial computed tomography (CT) scan showed a ventriculo-peritoneal shunt catheter in place. He was diagnosed with parasomnia following standard polysomnography in an external sleep laboratory. In our institution, polysomnography and 10–20 video EEG monitoring were performed for four nights, and 13 of the habitual attacks were recorded. All attacks occurred out of sleep with six attacks occurring from slow-wave sleep (N3) and seven occurring out of light sleep (N2). All but one attack were lacking an ictal EEG change, only one event was associated with rhythmic evolving theta in the left frontal region lasting for a few seconds. Tachykardia of up to 170 beats per minute was seen in all events. Video review revealed that the sequence of clinical features was extremely stereotypical and monomorphic across all 13 events. Interictal EEG was completely normal during the awake stage as well as during sleep in the first and second nights. Only in nights three and four, interictal sleep EEG revealed several spikes in the left frontal region. We diagnosed left frontal lobe epilepsy with exclusively sleep-related hypermotor seizures and stared the patient on anticonvulsant medication. This case demonstrates that the differential diagnosis of epilepsy versus Non-REM parasomnia may be difficult even with 10–20 video EEG. The vast majority of seizures (or all) may lack an ictal EEG pattern, and interictal epileptiform spikes or sharp waves may be very infrequent or absent. Short events with a very stereotypic sequence of clinical features as well as attacks arising from light sleep (N2) support the diagnosis of epilepsy. One or two nights of polysomnography – even with an extended 10–20 EEG montage – may not be sufficient to differentiate parasomnia from epileptic seizures.

Spotlight on the February 10 Issue

#### Notable in Neurology This issue features articles on 3-tesla functional MR language mapping compared to direct cortical stimulation in gliomas and on GRIN2A , an aptly named gene for speech dysfunction. Other featured articles focus on gamma frequency oscillations preceding interictal epileptiform spikes in seizure onset zones and on colonic mucosal α-synuclein lacking specificity as a biomarker for Parkinson disease. ### Peripheral nerve ultrasound in pediatric Charcot-Marie-Tooth disease type 1A![Graphic][1]</img> Nerve ultrasound is a noninvasive and well-tolerated peripheral nerve imaging modality. This study of 29 children with Charcot-Marie-Tooth disease type 1A (CMT1A) and 29 controls demonstrated a marked increase in … [1]: /embed/inline-graphic-1.gif

Gamma oscillations precede interictal epileptiform spikes in the seizure onset zone.

To investigate the generation, spectral characteristics, and potential clinical significance of brain activity preceding interictal epileptiform spike discharges (IEDs) recorded with intracranial EEG. Seventeen adult patients with drug-resistant temporal lobe epilepsy were implanted with intracranial electrodes as part of their evaluation for epilepsy surgery. IEDs detected on clinical macro- and research microelectrodes were analyzed using time-frequency spectral analysis. Gamma frequency oscillations (30-100 Hz) often preceded IEDs in spatially confined brain areas. The gamma-IEDs were consistently observed 35 to 190 milliseconds before the epileptiform spike waveforms on individual macro- and microelectrodes. The gamma oscillations associated with IEDs had longer duration (p < 0.001) and slightly higher frequency (p = 0.045) when recorded on microelectrodes compared with clinical macroelectrodes. Although gamma-IEDs comprised only a subset of IEDs, they were strongly associated with electrodes in the seizure onset zone (SOZ) compared with the surrounding brain regions (p = 0.004), in sharp contrast to IEDs without preceding gamma oscillations that were often also detected outside of the SOZ. Similar to prior studies, isolated pathologic high-frequency oscillations in the gamma (30-100 Hz) and higher (100-600 Hz) frequency range, not associated with an IED, were also found to be associated with SOZ. The occurrence of locally generated gamma oscillations preceding IEDs suggests a mechanistic role for gamma in pathologic network activity generating IEDs. The results show a strong association between SOZ and gamma-IEDs. The potential clinical application of gamma-IEDs for mapping pathologic brain regions is intriguing, but will require future prospective studies.

Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.

ObjectivesHigh frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue. The exact characteristics of clinically relevant HFOs and their detection are still to be defined.MethodsWe propose a new method for HFO detection, which we have applied to six patient iEEGs. In a first stage, events of interest (EoIs) in the iEEG were defined by thresholds of energy and duration. To recognize HFOs among the EoIs, in a second stage the iEEG was Stockwell-transformed into the time-frequency domain, and the instantaneous power spectrum was parameterized. The parameters were optimized for HFO detection in patient 1 and tested in patients 2–5. Channels were ranked by HFO rate and those with rate above half maximum constituted the HFO area. The seizure onset zone (SOZ) served as gold standard.ResultsThe detector distinguished HFOs from artifacts and other EEG activity such as interictal epileptiform spikes. Computation took few minutes. We found HFOs with relevant power at frequencies also below the 80–500 Hz band, which is conventionally associated with HFOs. The HFO area overlapped with the SOZ with good specificity > 90% for five patients and one patient was re-operated. The performance of the detector was compared to two well-known detectors.ConclusionsCompared to methods detecting energy changes in filtered signals, our second stage - analysis in the time-frequency domain - discards spurious detections caused by artifacts or sharp epileptic activity and improves the detection of HFOs. The fast computation and reasonable accuracy hold promise for the diagnostic value of the detector.

What is the importance of abnormal "background" activity in seizure generation?

Investigations of interictal epileptiform spikes and seizures have played a central role in the study of epilepsy. The background EEG activity, however, has received less attention. In this chapter we discuss the characteristic features of the background activity of the brain when individuals are at rest and awake (resting wake) and during sleep. The characteristic rhythms of the background EEG are presented, and the presence of 1/f (β) behavior of the EEG power spectral density is discussed and its possible origin and functional significance. The interictal EEG findings of focal epilepsy and the impact of interictal epileptiform spikes on cognition are also discussed.