The Clinical Utility of Finding Unexpected Subclinical Spikes Detected by High-Density EEG During Neurodiagnostic Investigations.

This study aimed to analyze the frequency of unexpected subclinical spikes (USCS) in pediatric patients who underwent high-density electroencephalogram (HD-EEG). Of the 4481 successful HD-EEG studies, 18.5% (829) were abnormal, and 49.7% of these abnormal studies showed SCS, of which 64.1% were USCS. USCS were found to be correlated with attention/concentration deficits and executive dysfunction, often accompanied by the dual psychiatric diagnosis of ADHD. MRI revealed abnormal findings in 32.6% of the subjects with USCS, such as abnormal signal or signal hyperintensity in brain parenchyma, temporal or arachnoid cysts, and vascular malformations. Moreover, the USCS group who received neuropsychiatric testing scored lower than the population mean on Full-Scale Intelligence Quotient, Working Memory Index, and Processing Speed Index. This study highlights the potential of USCS as biomarkers that can lead to changes in clinical management and outcomes, provide valuable information about pathophysiological mechanisms, and suggest potential treatment pathways.

Duration of N1 sleep is a factor for excessive daytime sleepiness in epilepsy patients with interictal epileptiform discharges: A polysomnographic study.

Purpose: This study aimed to identify the occurrence of excessive daytime sleepiness (EDS) in epilepsy patients with interictal epileptiform discharges and to explore the impact of interictal sleep architecture and sleep-related events on EDS. Methods: This study included 101 epilepsy patients with interictal epileptiform discharges (IED) and 100 control patients who underwent simultaneous polysomnography and video ambulatory electroencephalography for >7 h throughout a single night. Multiple sleep latency tests were used to assess EDS. Comorbid EDS was present in 25 and 11 patients in the IED epilepsy and control groups, respectively. In addition, univariate and multivariate logistic regression analyses were performed to explore the factors influencing EDS. Results: The epilepsy group had a higher prevalence of comorbid EDS and shorter R sleep duration. Univariate logistic regression analysis indicated that an increased risk of EDS may be associated with prolonged N1 sleep duration, higher arousal index, lower mean saturation (mSaO2), higher oxygen desaturation index (ODI), and duration of wake after sleep onset (WASO). Multivariate logistic regression analysis revealed that N1 sleep duration was significantly correlated with EDS. Conclusion: In epilepsy patients with IED, the arousal index, mSaO2, ODI, and duration of WASO were weakly correlated with EDS, and the duration of N1 sleep demonstrated a significant positive correlation with EDS, which requires further research.

When the Crown has Thorns - Epileptic Spike-Wave Discharges over the Vertex.

Epileptiform abnormalities that arise over the midline can sometimes be confused with normal sleep transients, such as vertex sharp waves, because of their location and their activation during sleep. However, epileptiform transients can be distinguished from sleep architecture by their waveform and their occurrence during wakefulness. Here, we report a 24-year-old man with drug-resistant epilepsy whose seizures began with tonic posturing of the left leg before progressing to bilateral tonic-clonic activity. During presurgical scalp video-EEG monitoring, his interictal background showed focal spike-wave discharges maximal over the vertex (phase reversal at Cz), with a more-well-defined field over the right parasagittal region (C4/F4), that were present during both sleep and awake states. The discharges met the IFCN criteria for focal interictal epileptiform discharges (spiky morphology, duration shorter than background activity, asymmetric waveform, after-going slow wave, and physiologic distribution) and appeared to be distinct from the patient's vertex sharp waves. Prior to electroclinical seizures, these discharges would increase in prevalence and appear as repetitive spike-wave discharges. When distinguishing epileptiform from nonepileptiform transients, it is critical to consider both their morphology, especially the degree of background disruption and presence of an after-going slow wave, and their variability with state changes.

The hemodynamic response to co-occurring interictal epileptiform discharges and high-frequency oscillations localizes the seizure-onset zone.

OBJECTIVE: To use intracranial electroencephalography (EEG) to characterize functional magnetic resonance imaging (fMRI) activation maps associated with high-frequency oscillations (HFOs) (80-250 Hz) and examine their proximity to HFO- and seizure-generating tissue. METHODS: Forty-five patients implanted with intracranial depth electrodes underwent a simultaneous EEG-fMRI study at 3 T. HFOs were detected algorithmically from cleaned EEG and visually confirmed by an experienced electroencephalographer. HFOs that co-occurred with interictal epileptiform discharges (IEDs) were subsequently identified. fMRI activation maps associated with HFOs were generated that occurred either independently of IEDs or within ±200 ms of an IED. For all significant analyses, the Maximum, Second Maximum, and Closest activation clusters were identified, and distances were measured to both the electrodes where the HFOs were observed and the electrodes involved in seizure onset. RESULTS: We identified 108 distinct groups of HFOs from 45 patients. We found that HFOs with IEDs produced fMRI clusters that were closer to the local field potentials of the corresponding HFOs observed within the EEG than HFOs without IEDs. In addition to the fMRI clusters being closer to the location of the EEG correlate, HFOs with IEDs generated Maximum clusters with greater z-scores and larger volumes than HFOs without IEDs. We also observed that HFOs with IEDs resulted in more discrete activation maps. SIGNIFICANCE: Intracranial EEG-fMRI can be used to probe the hemodynamic response to HFOs. The hemodynamic response associated with HFOs that co-occur with IEDs better identifies known epileptic tissue than HFOs that occur independently.

Combined value of interictal markers and stimulated seizures to estimate the seizure onset zone in stereoelectroencephalography.

OBJECTIVE: This study was undertaken to investigate the potential of interictal electroencephalographic (EEG) findings and electrically stimulated seizures during stereo-EEG (SEEG) as surrogate markers for the spontaneous seizure onset zone (spSOZ). We hypothesized that combining the localizing information of these markers would allow clinically meaningful estimation of the spSOZ. METHODS: We included all patients (n = 63) who underwent SEEG between January 2013 and March 2020 at Helsinki University Hospital and had spontaneous seizures during the recording. We scored spikes, gamma activity, and background abnormality on each channel visually during a 12-h epoch containing waking state and sleep. Based on semiology, we classified stimulated seizures as typical or atypical/unclassifiable and estimated the stimulated SOZ (stimSOZ) for typical seizures. To assess which markers increased the odds of channel inclusion in the spSOZ, we fitted mixed effects logistic regression models. RESULTS: A combined regression model including the stimSOZ and interictal markers scored during sleep performed better in estimating which channels were part of the spSOZ than models based on stimSOZ (p < .001) or interictal markers (p < .001) alone. Of the individual markers, the effect sizes were greatest for inclusion of a channel in the stimSOZ (odds ratio [OR] = 60, 95% confidence interval [CI] = 37-97, p < .001) and for continuous (OR = 25, 95% CI = 12-55, p < .001) and subcontinuous (OR = 36, 95% CI = 21-64, p < .001) interictal spiking. At the individual level, the model's accuracy to predict spSOZ inclusion varied markedly (median accuracy = 85.7, range = 54.4-100), which was not explained by etiology (p > .05). SIGNIFICANCE: Compared to either marker alone, combining visually rated interictal SEEG markers and stimulated seizures improved prediction of which SEEG channels belonged to the spSOZ. Inclusion in the stimSOZ and continuous or subcontinuous spikes increased the odds of spSOZ inclusion the most. Future studies should investigate whether suboptimal sampling of the true epileptogenic zone can explain the model's poor performance in certain patients.

Slow wave sleep is associated with forgetting in patients with temporal lobe epilepsy.

While time spent in slow wave sleep (SWS) after learning promotes memory consolidation in the healthy brain, it is unclear if the same benefit is obtained in patients with temporal lobe epilepsy (TLE). Interictal epileptiform discharges (IEDs) are potentiated during SWS and thus may disrupt memory consolidation processes thought to depend on hippocampal-neocortical interactions. Here, we explored the relationship between SWS, IEDs, and overnight forgetting in patients with TLE. Nineteen patients with TLE studied object-scene pairs and memory was tested across a day of wakefulness (6 hrs) and across a night of sleep (16 hrs) while undergoing continuous scalp EEG monitoring. We found that time spent in SWS after learning was related to greater forgetting overnight. Longer duration in SWS and number of IEDs were each associated with greater forgetting, although the number of IEDs did not mediate the relationship between SWS and memory. Further research, particularly with intracranial recordings, is required to identify the mechanisms by which SWS and IEDs can be pathological to sleep-dependent memory consolidation in patients with TLE.

Benign epileptiform variants in EEG: A comprehensive study of 3000 patients.

BACKGROUND: The analysis of EEG demands expertise and keen observation to distinguish epileptiform discharges from benign epileptiform variants (BEVs), a frequent source of erroneous interpretation. The prevalence of BEVs varies based on geographical, racial, and ethnic characteristics. However, most data on BEVs originates from Western populations, and additional studies on different cohorts would enrich the existing literature. METHODS: We reviewed EEGs from our institutional database to study the prevalence of benign epileptiform variants and analyzed their frequency, topography, and other characteristics. Additionally, we investigated the co-existence of epileptiform discharges with BEVs. RESULTS: We identified 296 patients with BEVs after reviewing 3000 EEGs (9.9%). The most common BEV was small sharp spikes (SSS), observed in 114 patients (3.8%). Wicket waves, 6 Hz spike and slow wave, 14 and 6 Hz positive bursts, and Rhythmic Temporal Theta of Drowsiness (RTTD) were identified in 67 (2.2%), 40 (1.3%), 39 (1.3%), and 35 (1.16%) patients, respectively and one patient with Subclinical Rhythmic EEG Discharges in Adults (SREDA). Additionally, we observed the co-existence of epileptiform discharges with BEVs, most commonly with SSS (27.8%). CONCLUSIONS: The present study is a large study with 3000 EEGs to describe the BEV characteristics. BEVs were seen in 9.9% of patients, BSSS being the most common. There were minor differences in frequency, gender or age distribution compared to existing literature. We demonstrated the co-existence of epileptiform discharges. Morphological characteristics remain the cornerstone in recognising BEVs. EEG readers need to be aware of features of BEVs to avoid wrongly interpretation.

Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings.

BACKGROUND: Scalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined. NEW METHOD: We recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared. RESULTS: Our results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters. COMPARISON WITH EXISTING METHODS: Our study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation. CONCLUSIONS: Simultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.

The significance of multimodality approach in the management of non-lesional drug-resistant focal parietal lobe epilepsies.

Due to extensive connectivity of the parietal lobe, non-lesional drug-resistant (DRE) parietal lobe epilepsies (PLEs) are difficult to localize and often imitate other epilepsies. Therefore, patients with PLEs have low rates of seizure freedom following epilepsy surgery. Previous studies have highlighted the need to combine EEG and semiology for more accurate localization of PLEs. As sophisticated tools for localization become more available, the use of multiple different neuroimaging and neurophysiologic diagnostic tests may more readily identify PLE. We hereby report a unique case of a complex localization in a non-lesional PLE, which was initially falsely localized to frontal lobe. This case underscores the utility of voxel-based morphometry (VBM) in identifying an epileptogenic lesion on a non-lesional MRI and the significance of multimodality approach including PET, magnetoencephalopathy (MEG), interictal and ictal EEG, semiology and cortical stimulation for accurate localization of PLEs. Understanding epilepsy through multimodality approach in this fashion can help with accurate localization especially in difficulty to localize and deceptive non-lesional PLEs. PLAIN LANGUAGE SUMMARY: Parietal lobe epilepsies are hard to pinpoint in the brain and can mimic other types of epilepsy, especially when brain MRIs appear normal. As sophisticated tools for locating epilepsies in the brain become more available, using multiple diagnostic tests may help identify parietal lobe epilepsies more easily. We describe a unique case of a parietal lobe epilepsy patient with normal brain MRI whose epilepsy was initially misidentified as being in the frontal lobe. Using various advanced diagnostic tests, we accurately found the epilepsy's true location in the parietal lobe and successfully treated the patient with surgery.

Interictal discharge traveling waves recorded from stereoelectroencephalography electrodes.

OBJECTIVE: Interictal epileptiform discharges (IEDs) are intermittent high-amplitude electrical signals that occur between seizures. They have been shown to propagate through the brain as traveling waves when recorded with epicortical grid-type electrodes and small penetrating microelectrode arrays. However, little work has been done to translate experimental IED analyses to more clinically relevant platforms such as stereoelectroencephalography (SEEG). In this pilot study, the authors aimed to define a computational method to identify and characterize IEDs recorded from clinical SEEG electrodes and leverage the directionality of IED traveling waves to localize the seizure onset zone (SOZ). METHODS: Continuous SEEG recordings from 15 patients with medically refractory epilepsy were collected, and IEDs were detected by identifying overlapping peaks of a minimum prominence. IED pathways of propagation were defined and compared to the SOZ location determined by a clinical neurologist based on the ictal recordings. For further analysis of the IED pathways of propagation, IED detections were divided into triplets, defined as a set of 3 consecutive contacts within the same IED detection. Univariate and multivariate linear regression models were employed to associate IED characteristics with colocalization to the SOZ. RESULTS: A median (range) of 22.6 (4.4-183.9) IEDs were detected per hour from 15 patients over a mean of 23.2 hours of recording. Depending on the definition of the SOZ, a median (range) of 20.8% (0.0%-54.5%) to 62.1% (19.2%-99.4%) of IEDs per patient traversed the SOZ. IEDs passing through the SOZ followed discrete pathways that had little overlap with those of the IEDs passing outside the SOZ. Contact triplets that occurred more than once were significantly more likely to be detected in an IED passing through the SOZ (p < 0.001). Per our multivariate model, patients with a greater proportion of IED traveling waves had a significantly greater proportion of IEDs that localized to the SOZ (ß = 0.64, 95% CI 0.01-1.27, p = 0.045). CONCLUSIONS: By using computational methods, IEDs can be meaningfully detected from clinical-grade SEEG recordings of patients with epilepsy. In some patients, a high proportion of IEDs are traveling waves according to multiple metrics that colocalize to the SOZ, offering hope that IED detection, with further refinement, could serve as an alternative method for SOZ localization.

EEG before chimeric antigen receptor T-cell therapy and early after onset of immune effector cell-associated neurotoxicity syndrome.

BACKGROUND: Immune effector cell-associated neurotoxicity syndrome (ICANS) is common after chimeric antigen receptor T-cell (CAR-T) therapy. OBJECTIVE: This study aimed to assess the impact of preinfusion electroencephalography (EEG) abnormalities and EEG findings at ICANS onset for predicting ICANS risk and severity in 56 adult patients with refractory lymphoma undergoing CAR-T therapy. STUDY DESIGN: EEGs were conducted at the time of lymphodepleting chemotherapy and shortly after onset of ICANS. RESULTS: Twenty-eight (50%) patients developed ICANS at a median time of 6 days after CAR-T infusion. Abnormal preinfusion EEG was identified as a risk factor for severe ICANS (50% vs. 17%, P = 0.036). Following ICANS onset, EEG abnormalities were detected in 89% of patients [encephalopathy (n = 19, 70%) and/or interictal epileptiform discharges (IEDs) (n = 14, 52%)]. Importantly, IEDs seemed to be associated with rapid progression to higher grades of ICANS within 24 h. CONCLUSIONS: If confirmed in a large cohort of patients, these findings could establish the basis for modifying current management guidelines, enabling the identification of patients at risk of neurotoxicity, and providing support for preemptive corticosteroid use in patients with both initial grade 1 ICANS and IEDs at neurotoxicity onset, who are at risk of neurological impairment.

Overcoming traps and pitfalls leading to misinterpretation of normal EEG variants and variation of the background activity.

Normal EEG variants, especially the epileptiform variants, can be challenging to interpret because they often have sharp contours and may be confused with "epileptic" interictal activities. However, they can be recognized by the fact that "most spikes or sharp wave discharges of clinical import are followed by a slow wave or a series of slow deflections" (Maulsby, 1971). If there is no wave after the spike, electroencephalographers should be suspicious of artifacts and normal EEG variants. Most normal EEG variants display a single rhythm with the same frequency within the pattern and the morphology remains stable throughout the entire EEG recording with repetition of the same pattern. In case of doubt or difficulties with a standard EEG, it is recommended to undergo an EEG that includes sleep stages with or without sleep deprivation. Finally, epileptiform is an ambiguous term corresponding to an electroencephalographic trait. Epileptiform does not imply a pathological condition, including epilepsy. The clinical context remains the most paramount in the diagnosis of epilepsy. In this article, we propose a set of rules and guidelines to identify normal EEG variants in EEG tracings and normal variation of the background activity. It is not easy to accurately assign a specific/precise name to all EEG activity, but with an orderly approach to EEG that involves using a set of criteria, nonepileptic activity can be identified.

A randomized controlled educational pilot trial of interictal epileptiform discharge identification for neurology residents.

OBJECTIVE: To assess the effectiveness of an educational program leveraging technology-enhanced learning and retrieval practice to teach trainees how to correctly identify interictal epileptiform discharges (IEDs). METHODS: This was a bi-institutional prospective randomized controlled educational trial involving junior neurology residents. The intervention consisted of three video tutorials focused on the six IFCN criteria for IED identification and rating 500 candidate IEDs with instant feedback either on a web browser (intervention 1) or an iOS app (intervention 2). The control group underwent no educational intervention ("inactive control"). All residents completed a survey and a test at the onset and offset of the study. Performance metrics were calculated for each participant. RESULTS: Twenty-one residents completed the study: control (n = 8); intervention 1 (n = 6); intervention 2 (n = 7). All but two had no prior EEG experience. Intervention 1 residents improved from baseline (mean) in multiple metrics including AUC (.74; .85; p < .05), sensitivity (.53; .75; p < .05), and level of confidence (LOC) in identifying IEDs/committing patients to therapy (1.33; 2.33; p < .05). Intervention 2 residents improved in multiple metrics including AUC (.81; .86; p < .05) and LOC in identifying IEDs (2.00; 3.14; p < .05) and spike-wave discharges (2.00; 3.14; p < .05). Controls had no significant improvements in any measure. SIGNIFICANCE: This program led to significant subjective and objective improvements in IED identification. Rating candidate IEDs with instant feedback on a web browser (intervention 1) generated greater objective improvement in comparison to rating candidate IEDs on an iOS app (intervention 2). This program can complement trainee education concerning IED identification.

Idiopathic generalized epilepsies in the epilepsy monitoring unit: Systematic quantification of focal EEG and semiological signs.

OBJECTIVE: Focal seizure symptoms (FSS) and focal interictal epileptiform discharges (IEDs) are common in patients with idiopathic generalized epilepsies (IGEs), but dedicated studies systematically quantifying them both are lacking. We used automatic IED detection and localization algorithms and correlated these EEG findings with clinical FSS for the first time in IGE patients. METHODS: 32 patients with IGEs undergoing long-term video EEG monitoring were systematically analyzed regarding focal vs. generalized IEDs using automatic IED detection and localization algorithms. Quantitative EEG findings were correlated with FSS. RESULTS: We observed FSS in 75% of patients, without significant differences between IGE subgroups. Mostly varying/shifting lateralizations of FSS across successive recorded seizures were seen. We detected a total of 81,949 IEDs, whereof 19,513 IEDs were focal (23.8%). Focal IEDs occurred in all patients (median 13% focal IEDs per patient, range 1.1 - 51.1%). Focal IED lateralization and localization predominance had no significant effect on FSS. CONCLUSIONS: All included patients with IGE showed focal IEDs and three-quarter had focal seizure symptoms irrespective of the specific IGE subgroup. Focal IED localization had no significant effect on lateralization and localization of FSS. SIGNIFICANCE: Our findings may facilitate diagnostic and treatment decisions in patients with suspected IGE and focal signs.

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.

To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) and 166 094 non-IED 4-second video-EEG segments. The video data is processed by the proposed patient detection method, with frame difference and Simple Keypoints (SKPS) capturing patients' movements. EEG data is processed with EfficientNetV2. The video and EEG features are fused via a multilayer perceptron. We developed a comparative model, termed nEpiNet, to test the effectiveness of the video feature in vEpiNet. The 10-fold cross-validation was used for testing. The 10-fold cross-validation showed high areas under the receiver operating characteristic curve (AUROC) in both models, with a slightly superior AUROC (0.9902) in vEpiNet compared to nEpiNet (0.9878). Moreover, to test the model performance in real-world scenarios, we set a prospective test dataset, containing 215 h of raw video-EEG data from 50 patients. The result shows that the vEpiNet achieves an area under the precision-recall curve (AUPRC) of 0.8623, surpassing nEpiNet's 0.8316. Incorporating video data raises precision from 70% (95% CI, 69.8%-70.2%) to 76.6% (95% CI, 74.9%-78.2%) at 80% sensitivity and reduces false positives by nearly a third, with vEpiNet processing one-hour video-EEG data in 5.7 min on average. Our findings indicate that video data can significantly improve the performance and precision of IED detection, especially in prospective real clinic testing. It suggests that vEpiNet is a clinically viable and effective tool for IED analysis in real-world applications.

Developmental outcome of electroencephalographic findings in SYNGAP1 encephalopathy.

SYNGAP1 haploinsufficiency results in a developmental and epileptic encephalopathy (DEE) causing generalized epilepsies accompanied by a spectrum of neurodevelopmental symptoms. Concerning interictal epileptiform discharges (IEDs) in electroencephalograms (EEG), potential biomarkers have been postulated, including changes in background activity, fixation-off sensitivity (FOS) or eye closure sensitivity (ECS). In this study we clinically evaluate a new cohort of 36 SYNGAP1-DEE individuals. Standardized questionnaires were employed to collect clinical, electroencephalographic and genetic data. We investigated electroencephalographic findings, focusing on the cortical distribution of interictal abnormalities and their changes with age. Among the 36 SYNGAP1-DEE cases 18 presented variants in the SYNGAP1 gene that had never been previously reported. The mean age of diagnosis was 8 years and 8 months, ranging from 2 to 17 years, with 55.9% being male. All subjects had global neurodevelopmental/language delay and behavioral abnormalities; 83.3% had moderate to profound intellectual disability (ID), 91.7% displayed autistic traits, 73% experienced sleep disorders and 86.1% suffered from epileptic seizures, mainly eyelid myoclonia with absences (55.3%). A total of 63 VEEGs were revised, observing a worsening of certain EEG findings with increasing age. A disorganized background was observed in all age ranges, yet this was more common among older cases. The main IEDs were bilateral synchronous and asynchronous posterior discharges, accounting for ≥50% in all age ranges. Generalized alterations with maximum amplitude in the anterior region showed as the second most frequent IED (≥15% in all age ranges) and were also more common with increasing age. Finally, diffuse fast activity was much more prevalent in cases with 6 years or older. To the best of our knowledge, this is the first study to analyze EEG features across different age groups, revealing an increase in interictal abnormalities over infancy and adolescence. Our findings suggest that SYNGAP1 haploinsufficiency has complex effects in human brain development, some of which might unravel at different developmental stages. Furthermore, they highlight the potential of baseline EEG to identify candidate biomarkers and the importance of natural history studies to develop specialized therapies and clinical trials.

V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges.

Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients with similar waveforms only. This study then proposes a dual-view learning framework (namely V2IED) to detect IED events from multi-channel EEG via aggregating features from the two phases: (1) Morphological Feature Learning: directly treating the EEG as a sequence with multiple channels, a 1D-CNN (Convolutional Neural Network) is applied to explicitly learning the deep morphological features; and (2) Spatial Feature Learning: viewing the EEG as a 3D tensor embedding channel topology, a CNN captures the spatial features at each sampling point followed by an LSTM (Long Short-Term Memories) to learn the evolution of these features. Experimental results from a public EEG dataset against the state-of-the-art counterparts indicate that: (1) compared with the existing optimal models, V2IED achieves a larger area under the receiver operating characteristic (ROC) curve in detecting IEDs from normal sharp transients with a 5.25% improvement in accuracy; (2) the introduction of spatial features improves performance by 2.4% in accuracy; and (3) V2IED also performs excellently in distinguishing IEDs from background signals especially benign variants.

Therapeutic Strategies in Children with Epilepsy: A Quality-of-Life-Related Perspective.

Back ground: Children with epilepsy are affected by several factors, including clinical and social variables. Among these variables, cognitive decline and behavioral disturbances, perceptions of stigma, and fatigue can lead to reductions in quality of life (QOL). Epileptic activities, including seizure severity, frequent seizures, and status epilepticus (SE), have been identified as important predictors of QOL. In addition, the frequency of interictal epileptiform discharges (IEDs) on electroencephalogram (EEG) may also be an important predictor of QOL, because IEDs can lead to cognitive decline and behavioral disturbances. Moreover, frequent seizures and/or IEDs may play a role in emotional mediators, such as stigma and fatigue, in childhood epilepsy. Seizure severity and/or IEDs are, therefore, important QOL-related factors in childhood epilepsy. Seizure severity as a QOL-related factor: Frontal lobe dysfunctions, such as cognitive decline and behavioral disturbances, can result in reduced QOL for both the child and their family. Frontal and prefrontal lobe growth disturbances can be present during active-phase epilepsy in some children with neuropsychological impairments. Recovery from prefrontal lobe growth disturbances may depend on the active seizure period. Children with a shorter active seizure period can recover from disturbances in prefrontal lobe growth more rapidly. In contrast, recovery may be delayed in children with a longer active seizure period. Moreover, frequent seizures can lead to seizure-associated headaches, perceptions of self-stigma and parental stigma, and fatigue. Accordingly, severe seizures can lead to neuropsychological impairments in association with prefrontal lobe growth disturbances in children with epilepsy. EEG abnormalities as QOL-related factors: IEDs on EEG, representing persistent pathological neuronal discharges, may be associated with several pathological aspects. Frontal IEDs can be a risk factor for recurrent seizures, cognitive decline, and behavioral disturbances, and they may also play a role as emotional mediators similar to stigma. In addition, behavioral disturbances may result in the presence of secondary bilateral synchrony (SBS) on EEG. Behavioral disturbances can be improved in association with a reduction in IEDs in children with frontal IEDs and SBS. Therefore, EEG abnormalities, such as frontal IEDs and SBS, can also lead to neuropsychological impairments in children with epilepsy. Therapeutic strategies in children with epilepsy: Seizure severity and IEDs on EEG may be associated with neuropsychological impairments, leading to QOL reduction. Therapeutic management may be desirable to reduce seizures and EEG abnormalities, such as frontal IEDs and SBS, as early as possible to improve QOL in children with epilepsy. During antiseizure medication (ASM) selection and adjustment, physicians should strategize the therapeutic approach to controlling seizures and suppressing EEG abnormalities in children with epilepsy. Among various ASMs, novel ASMs, such as levetiracetam and perampanel, may suppress both clinical seizures and IEDs on EEG; thus, these novel ASMs may represent an important addition to the treatments available for epileptic children presenting with frontal IEDs and SBS.

Interrater reliability of interictal EEG waveforms in Lennox-Gastaut Syndrome.

OBJECTIVE: Identification of EEG waveforms is critical for diagnosing Lennox-Gastaut Syndrome (LGS) but is complicated by the progressive nature of the disease. Here, we assess the interrater reliability (IRR) among pediatric epileptologists for classifying EEG waveforms associated with LGS. METHODS: A novel automated algorithm was used to objectively identify epochs of EEG with transient high power, which were termed events of interest (EOIs). The algorithm was applied to EEG from 20 LGS subjects and 20 healthy controls during NREM sleep, and 1350 EOIs were identified. Three raters independently reviewed the EOIs within isolated 15-second EEG segments in a randomized, blinded fashion. For each EOI, the raters assigned a waveform label (spike and slow wave, generalized paroxysmal fast activity, seizure, spindle, vertex, muscle, artifact, nothing, or other) and indicated the perceived subject type (LGS or control). RESULTS: Labeling of subject type had 85% accuracy across all EOIs and an IRR of κ =0.790, suggesting that brief segments of EEG containing high-power waveforms can be reliably classified as pathological or normal. Waveform labels were less consistent, with κ =0.558, and the results were highly variable for different categories of waveforms. Label mismatches typically occurred when one reviewer selected "nothing," suggesting that reviewers had different thresholds for applying named labels. SIGNIFICANCE: Classification of EEG waveforms associated with LGS has weak IRR, due in part to varying thresholds applied during visual review. Computational methods to objectively define EEG biomarkers of LGS may improve IRR and aid clinical decision-making.

Learn how to interpret and use intracranial EEG findings.

Epilepsy surgery is the therapy of choice for many patients with drug-resistant focal epilepsy. Recognizing and describing ictal and interictal patterns with intracranial electroencephalography (EEG) recordings is important in order to most efficiently leverage advantages of this technique to accurately delineate the seizure-onset zone before undergoing surgery. In this seminar in epileptology, we address learning objective "1.4.11 Recognize and describe ictal and interictal patterns with intracranial recordings" of the International League against Epilepsy curriculum for epileptologists. We will review principal considerations of the implantation planning, summarize the literature for the most relevant ictal and interictal EEG patterns within and beyond the Berger frequency spectrum, review invasive stimulation for seizure and functional mapping, discuss caveats in the interpretation of intracranial EEG findings, provide an overview on special considerations in children and in subdural grids/strips, and review available quantitative/signal analysis approaches. To be as practically oriented as possible, we will provide a mini atlas of the most frequent EEG patterns, highlight pearls for its not infrequently challenging interpretation, and conclude with two illustrative case examples. This article shall serve as a useful learning resource for trainees in clinical neurophysiology/epileptology by providing a basic understanding on the concepts of invasive intracranial EEG.