EEG Seizure Research Articles

The net impact of clinical seizures on outcome characteristics in infants with neonatal encephalopathies at 12 months of age

PurposeTo assess the impact of clinical neonatal seizures on outcome characteristics of preterm and term newborns with neonatal encephalopathy (NE). MethodsWe designed a prospective comparative study with 53 babies (preterm neonates: 26 and term neonates: 27) with NE: group 1 (preterm neonates with seizures, n = 13), group 2 (preterm neonates without seizures, n = 13), group 3 (term neonates with seizures, n = 13) and group 4 (term neonates without seizures, n = 14). The functional outcome characteristics of the survivors were assessed by the Ankara Developmental Screening Inventory (ADSI) and the Guide for Monitoring Child Development (GMCD) at 12 months of age. ResultsClinically defined acute symptomatic seizures were diagnosed with prompt conventional EEG / amplitude-integrated EEG in preterm (92.3%) and term neonates (81.4%) with etiology-specific diagnoses of NE. There were no differences between the study groups regarding seizure semiology and EEG characteristics. A primary adverse outcome was defined in 22 (41.5%) of the cohort. However, only 15.3% of infants had an unfavorable functional outcome with ADSI at 12 months. Among the survivors, there was no significant difference between the study groups regarding ADSI scores. The GMDC test revealed normal development in 50% of survivors with seizures in the preterm group and 83% in the term group. ConclusionThere was no significant difference between the characteristics of functional outcomes at 12 months in preterm and term neonates with NE for clinical seizures.

Interactive local and global feature coupling for EEG-based epileptic seizure detection

Automatic seizure detection based on scalp electroencephalogram (EEG) can accelerate the progress of epilepsy diagnosis. Current seizure detection methods based on deep learning usually rely on single convolutional neural network (CNN) or recurrent neural network (RNN) models. In terms of feature extraction, single model often has limitations. Since CNN is good at extracting local features, and Transformer can capture global information, we put forward a seizure detection method based on interactive local and global feature coupling. Local feature and global representation of the EEG are respectively extracted by convolution operation and self-attention mechanism. To make up of the shortcomings of local feature and global representation, a feature coupling block (FCB) is utilized to fuse the two kinds of information in an interactive way. The enhanced feature representation is fed to the classifier for seizure and normal EEG classification. Extensive experiments are conducted on CHB-MIT and Siena scalp EEG datasets. Experimental results demonstrate that the method can effectively perform epileptic seizure detection from the original EEG signals without extra feature extraction.

Interictal epileptiform discharges as a predictive biomarker for recurrence of poststroke epilepsy.

Poststroke epilepsy is a major ischaemic/haemorrhagic stroke complication. Seizure recurrence risk estimation and early therapeutic intervention are critical, given the association of poststroke epilepsy with worse functional outcomes, quality of life and greater mortality. Several studies have reported risk factors for seizure recurrence; however, in poststroke epilepsy, the role of EEG in predicting the risk of seizures remains unclear. This multicentre observational study aimed to clarify whether EEG findings constitute a risk factor for seizure recurrence in patients with poststroke epilepsy. Patients with poststroke epilepsy were recruited from the PROgnosis of POst-Stroke Epilepsy study, an observational multicentre cohort study. The enrolled patients with poststroke epilepsy were those admitted at selected hospitals between November 2014 and June 2017. All patients underwent EEG during the interictal period during admission to each hospital and were monitored for seizure recurrence over 1 year. Board-certified neurologists or epileptologists evaluated all EEG findings. We investigated the relationship between EEG findings and seizure recurrence. Among 187 patients with poststroke epilepsy (65 were women with a median age of 75 years) admitted to the lead hospital, 48 (25.7%) had interictal epileptiform discharges on EEG. During the follow-up period (median, 397 days; interquartile range, 337-450 days), interictal epileptiform discharges were positively correlated with seizure recurrence (hazard ratio, 3.82; 95% confidence interval, 2.09-6.97; P < 0.01). The correlation remained significant even after adjusting for age, sex, severity of stroke, type of stroke and generation of antiseizure medications. We detected periodic discharges in 39 patients (20.9%), and spiky/sharp periodic discharges were marginally associated with seizure recurrence (hazard ratio, 1.85; 95% confidence interval, 0.93-3.69; P = 0.08). Analysis of a validation cohort comprising 187 patients with poststroke epilepsy from seven other hospitals corroborated the association between interictal epileptiform discharges and seizure recurrence. We verified that interictal epileptiform discharges are a risk factor for seizure recurrence in patients with poststroke epilepsy. Routine EEG may facilitate the estimation of seizure recurrence risk and the development of therapeutic regimens for poststroke epilepsy.

Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction.

Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced technique for seizure prediction. Recent deep learning approaches, which fail to fully explore both the characterizations in EEGs themselves and correlations among different electrodes simultaneously, generally neglect the spatial or temporal dependencies in an epileptic brain and, thus, produce suboptimal seizure prediction performance consequently. To tackle this issue, in this article, a patient-specific EEG seizure predictor is proposed by using a novel spatio-temporal-spectral hierarchical graph convolutional network with an active preictal interval learning scheme (STS-HGCN-AL). Specifically, since the epileptic activities in different brain regions may be of different frequencies, the proposed STS-HGCN-AL framework first infers a hierarchical graph to concurrently characterize an epileptic cortex under different rhythms, whose temporal dependencies and spatial couplings are extracted by a spectral-temporal convolutional neural network and a variant self-gating mechanism, respectively. Critical intrarhythm spatiotemporal properties are then captured and integrated jointly and further mapped to the final recognition results by using a hierarchical graph convolutional network. Particularly, since the preictal transition may be diverse from seconds to hours prior to a seizure onset among different patients, our STS-HGCN-AL scheme estimates an optimal preictal interval patient dependently via a semisupervised active learning strategy, which further enhances the robustness of the proposed patient-specific EEG seizure predictor. Competitive experimental results validate the efficacy of the proposed method in extracting critical preictal biomarkers, indicating its promising abilities in automatic seizure prediction.

Lennox Gastaut Syndrome – A strategic shift in diagnosis over time?

BackgroundLennox Gastaut Syndrome (LGS) is an epilepsy syndrome presenting in childhood, classically characterised by a triad of cognitive or developmental impairment, multiple seizure types and EEG features of slow-spike waves (SWW), with or without paroxysmal fast activity (PFA) in sleep. There is increasing scientific opinion in favour of a less rigid approach to LGS diagnosis and this clinical audit attempts to shed light on how the LGS diagnostic criteria used may have changed over time, in a large tertiary paediatric neurology unit (Great Ormond Street Hospital (GOSH), London, UK). MethodsElectronic patient records were reviewed for patients with a diagnosis of LGS made at GOSH within two time periods, 2014-2017 and 2018-2021. Patient demographics, aetiology, clinical features, EEG features, investigation results and medications were reviewed. Findings were compared against the International League Against Epilepsy (ILAE) Diagnostic Manual criteria for LGS diagnosis (the classical triad plus PFA in sleep). Existing GOSH data regarding total number of new epilepsy referrals to GOSH and ICD10 codes (including all epilepsy and epilepsy syndromes) per year were also used to review LGS diagnoses as proportions of new epilepsy referrals and total number of epilepsy diagnoses. Results5 LGS diagnoses were made in 2014-2017 compared to 40 diagnoses made in 2018-2021. There was a steep increase in the number of LGS diagnoses and LGS diagnoses as a proportion of total epilepsy diagnoses in the last 4 years, coinciding with the licensing of cannabidiol for management of LGS in the UK in 2018. There was a much less marked increase in LGS diagnoses as a proportion of epilepsy referrals from 2018 (and an as yet unexplained high proportion in 2014). The 2014-17 cohort fit a more classical LGS diagnostic criteria of the triad plus presence of PFA on sleep EEG (100% in 2014-17 vs 68% in 2018-21), with a more classical preceding history of infantile spasms (80% in 2014-17 vs 23% in 2018-21). Common seizure types were similar in both groups and a male preponderance was also noted in both groups. Cannabidiol use in LGS patients has also increased over time, all prescribed after 2018. ConclusionsDespite the limitations of the small numbers of patients in each group, the results of this clinical audit are in keeping with changing clinical trends in favour of a broader LGS diagnostic criteria. The combination of this less rigid diagnostic criteria, the steep increase in diagnosis seen from 2018 onwards (coinciding with UK cannabidiol licencing for LGS in 2018) and the fact that most of the LGS patients in both groups are currently prescribed cannabidiol, may reflect a strategic shift in attitude towards LGS diagnosis or an increase in referrals for LGS, possibly to help facilitate access to novel treatments.

Does the Use of Rocuronium-Sugammadex Instead of Succinylcholine in Electroconvulsive Therapy Affect Seizure Duration?

We compared retrospectively the seizure variables of electroconvulsive therapy (ECT) patients after administration of rocuronium-sugammadex or succinylcholine as a muscle relaxant with propofol anesthesia. The sample comprised 134 ECT patients. The mean age was 33.6±10.48 years. Anesthesia induction was provided with propofol one mg kg-intravenously (IV) followed by succinylcholine 0.5 mg kg-1 IV (n=68) (Group S) or rocuronium 0.3 mg kg-1 IV (n=66) (Group R). For patients who were given rocuronium, reversal of the residual neuromuscular block was accomplished with sugammadex (1.5 mg kg-1 IV). First session seizure variables were compared between the two groups. We also presented the clinical outcome with Clinical Global Impression-Improvement (CGI-I) and overall adverse effects. EEG seizure durations in Group R (55.09±36.11 s) and Group S (47.00±26.33 s) were comparable and were not significantly different (p=0.432). The clinical efficacy of ECT measured by CGI-I in both groups was comparable (p=0.075). There were no major complications or death during or after ECT. The results of this study show that the use of rocuronium-sugammadex as a neuromuscular blocker instead of succinylcholine during ECT with propofol anesthesia produces similar results in terms of seizure variables and clinical outcomes.

Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research.

Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.

MRI based composite parameter of multiple tissue types for improved patient-level hemispheric and regional level lateralization in pediatric epilepsy

Although voxel-based morphometry (VBM) of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) changes aid in epileptic seizure lateralization, type of T1 pulse sequence, preprocessing steps and tissue segmentation methods lead to variation in tissue classification. Here, we test the prediction accuracy of individual MRI based tissue types and a novel composite ratio parameter [(GM + WM)/CSF], sensitive to parenchymal changes and independent of tissue classification variations. Pediatric patients with partial seizures (both simple and complex), but normal and lesion-free MRI were considered (33 patients; unilateral EEG; 17 female / 16 male; age mean ± SD = 11.5 ± 5 years). MRI based seizure lateralization was performed for each patient and verified with EEG findings alone or in combination with seizure semiology. T1 weighted MRI from patients and normal control subjects was spatially transformed to the Talairach atlas and automatically segmented into GM, WM and CSF tissue types. 41 age matched normal controls (11 female / 30 male; age mean ± SD = 14.6 ± 3 years) served as the null distribution to test tissue type deviations across each epilepsy patient. When verified with the patient EEG prediction, WM, GM and CSF had a hemispheric match of 76%, 70% and 55% respectively, while the composite ratio [(GM + WM)/CSF)] showed the highest accuracy of 85%. When EEG findings and seizure semiology were combined, MRI predictions using the composite ratio improved further to 88%. To further localize the epileptic focus, regional level (frontal, temporal, parietal and occipital) MRI predictions were obtained. The composite ratio performed at 88–91% accuracy, revealing regional MRI changes, not predictable with EEG. The results show inconsistent changes in GM and WM in majority of the pediatric epilepsy patients and demonstrate the applicability of the composite ratio [(GM + WM)/CSF)] as a superior predictor, independent of tissue classification variations. Clinical EEG findings combined with seizure semiology, can overcome scalp EEG's limitations and lean towards the MRI lateralization in specific cases.

Epileptiform abnormalities in the disconnected hemisphere are common in seizure-free patients after hemispherectomy

The spectrum of EEG abnormalities in the disconnected hemisphere in seizure-free patients after hemispherectomy has not been well characterized. Fifty consecutive patients who were seizure-free following hemispheric disconnection were identified. Data on preoperative clinical, EEG and neuroimaging findings and postoperative EEG findings and antiseizure medication status were collected. Forty-seven patients (94%) had functional hemispherectomy, and three (6%) had more extensive tissue resection of the posterior quadrant or frontal region along with hemispheric disconnection. Etiologies included: residual effects from vascular lesions including perinatal stroke in 35 patients, Rasmussen encephalitis in six, malformation of cortical development in seven, and Sturge-Weber syndrome in two. Pre-operative EEG showed focal epileptiform discharges in the affected hemisphere in 26 patients and in both hemispheres in 19. Eleven patients had diffuse bisynchronous epileptiform discharges. Postoperative EEGs at six to 12 months after surgery showed slowing and attenuation of physiological rhythms on the operated side in all patients. Thirty-four patients (68%) had epileptiform discharges exclusively from the operated hemisphere, six (12%) had bilateral independent epileptiform discharges, nine (18%) had no epileptiform discharges on either side, and one (2%) had epileptiform discharges from the contralateral hemisphere only. Lateralized periodic discharges from the operated hemisphere were noted in three patients (6%). EEG seizures from the operated hemisphere without clinical signs were noted in four patients (8%). After a median follow-up of three years, 30 patients (60%) were off all antiseizure medications, including 8/9 (89%) patients with no epileptiform discharges, 20/34 (59%) patients with postoperative epileptiform discharges from the operated hemisphere, and 2/7 (28%) patients with contralateral discharges. The majority of patients who are seizure-free after disconnective hemispherectomy will continue to show epileptiform discharges in the operated hemisphere. The presence of such discharges should not preclude tapering, nor prompt restarting of antiseizure medication in seizure-free patients.

VP.78 Clinical and genetic study of LAMA2-related muscular dystrophy patients with seizures

<i>LAMA2</i>-related muscular dystrophy (LAMA2-MD) is an autosomal recessive muscular dystrophy caused by pathogenic variants in <i>LAMA2</i> gene, including two subgroups: severe, early-onset <i>LAMA2</i>-related congenital muscular dystrophy (LAMA2-CMD) and mild, late-onset autosomal recessive limb-girdle muscular dystrophy-23 (LGMD R23). Seizures frequently occurred in LAMA2-MD, however, the relationship between seizures and genotype in LAMA2-MD is not fully known. The clinical information of seizures including seizure type, frequency, treament, EEG changes, brain MRI and genetic data of LAMA2-MD patients from Peking University First Hospital between January 2003 and March 2022 were collected and analyzed. Among 141 LAMA2-MD patients, seizures were observed in 10.5% (13/124) of LAMA2-CMD patients and 29.4% (5/17) of LGMD R23 patients, including 14 patients with epilepsy and 4 patients with febrile seizures. The median (range) age of first seizure episode was 5 (0.1-20) years in LAMA2-CMD and 14 (1-24) years in LGMD R23. Eight patients with epilepsy showed focal seizures. Abnormal results of electroencephalography were found in 8/10 patients, manifesting as temporal, occipital or temporo-occipital epileptiform abnormalities. Brain magnetic resonance imaging (MRI) was performed in 16 patients with seizures, showing occipital pachygyria in 4 patients, cerebellum and brain stem dysplasia in 2 patients. Brain MRI of 41 LAMA2-MD patients without seizures were reassessed, showing occipital lobe pachygyria in 6 patients. Therefore, there was no significant association between seizures and occipital pachygyria in our cohort (P=0.18). Nine patients received antiepileptic drugs, while patients with occipital lobe pachygyria showed poor seizure control. The pathogenic variants included splice mutation (n=8), nonsense mutation (n=6) and frameshift mutation(n=6) in LAMA2-CMD patients, while missense mutation (n=5), frameshift mutation(n=4) in LGMD R23 patients. Compared with LAMA2-CMD, seizures were more frequently occurred in LGMD R23 patients and mainly manifested as focal seizures. No correlation between genotype and seizures was observed. In this study, we found that seizures occurred in 10.5% of LAMA2-CMD and 29.4% of LGMD R23 patients, and described the features of seizures in LAMA2-MD patients.

Ictal-Interictal Epileptic State Classification with Traditional and Deep Learning Architectures

Epileptic seizures are caused by disturbances in the electrical activity of the brain. Failure to correctly classify epileptic forms may result in inappropriate treatment. Activities occurring prior to ictal activity may be causal and require further investigation. Therefore, it is important in the diagnosis of epilepsy to distinguish between ictal and interictal EEG using Electroencephalography (EEG) signs. In this study, ictal (absence seizure) and interictal EEG recordings were scored using 4 bipolar (C3-P3, T5-O1, FP2-F8, C4-T4) channel EEGs from Temple University Hospital (TUH) EEG Seizure Corpus (TUSZ) data. The data were divided into 3-second epochs and various features were obtained from the data. The data in each epoch were filtered using the Discrete Wavelet Transform (DWT) Daubechies-2 wavelet and were 0-32 Hz. range has been studied. Feature selection was made with Correlation based Feature Selection (CFS). The performances of traditional and deep learning classifier algorithms (Support vector machine (SVM), Long Short-term Memory (LSTM)) were compared and the results were discussed. The highest success rate was 96.96% in 84.86 seconds with the LSTM classifier model and 96.36% in 0.05 seconds with the SVM classifier algorithm

Seizure Burden, EEG, and Outcome in Neonates With Acute Intracranial Infections: A Prospective Multicenter Cohort Study

BackgroundLimited data exist regarding seizure burden, electroencephalogram (EEG) background, and associated outcomes in neonates with acute intracranial infections. MethodsThis secondary analysis was from a prospective, multicenter study of neonates enrolled in the Neonatal Seizure Registry with seizures due to intracranial infection. Sites used continuous EEG monitoring per American Clinical Neurophysiology Society guidelines. High seizure burden was defined a priori as seven or more EEG-confirmed seizures. EEG background was categorized using standardized terminology. Primary outcome was neurodevelopment at 24-months corrected age using Warner Initial Developmental Evaluation of Adaptive and Functional Skills (WIDEA-FS). Secondary outcomes were postneonatal epilepsy and motor disability. ResultsTwenty-seven of 303 neonates (8.9%) had seizures due to intracranial infection, including 16 (59.3%) bacterial, 5 (18.5%) viral, and 6 (22.2%) unknown. Twenty-three neonates (85%) had at least one subclinical seizure. Among 23 children with 24-month follow-up, the WIDEA-FS score was, on average, 23 points lower in children with high compared with low seizure burden (95% confidence interval, [−48.4, 2.1]; P = 0.07). After adjusting for gestational age, infection etiology, and presence of an additional potential acute seizure etiology, the effect size remained unchanged (β = −23.8, P = 0.09). EEG background was not significantly associated with WIDEA-FS score. All children with postneonatal epilepsy (n = 4) and motor disability (n = 5) had high seizure burden, although associations were not significant. ConclusionHigh seizure burden may be associated with worse neurodevelopment in neonates with intracranial infection and seizures. EEG monitoring can provide useful management and prognostic information in this population.

Intercorrelation of physiological seizure parameters and hormonal changes in electroconvulsive therapy

Objective Physiological parameters that predict electroconvulsive therapy (ECT) effectiveness may reflect propagation of the induced epileptic seizure. As an indication of seizure propagation to the diencephalon, we here examined the correlation between prolactin increase after ECT and clinical seizure evaluation parameters, focusing on peak heart rate. As a proxy for peripheral endocrine stress response, we examined the correlation to postictal cortisol increase. Methods Participants were consecutively recruited from clinical ECT patients (n = 131, age 18–85 years). The first ECT session in a series was examined. For each participant, blood serum concentrations of prolactin and cortisol were measured immediately before and within 30 min after the seizure. Physiological parameters were extracted from clinical records: peak heart rate (HR) during seizure, electroencephalography (EEG) seizure duration, and motor seizure duration. Correlations were calculated using non-parametric tests. Results Serum prolactin increased after ECT and correlated with peak HR, EEG seizure duration, and motor seizure duration. Peak HR during seizure also correlated positively with both EEG seizure duration and motor seizure duration. Correlations were unaffected by age, sex, baseline prolactin levels, antipsychotics, or beta-blocking agents. Serum cortisol increased after ECT but did not correlate with the seizure evaluation parameters, nor with prolactin concentrations. Conclusions Our findings of a positive correlation between peak HR and prolactin that was independent from the peripheral endocrine stress response might be in line with the idea that tachycardia during ECT seizures reflects seizure propagation to the diencephalon. This supports the practice of monitoring cardiovascular response for ECT seizure evaluation.

Efficacy of pulse intravenous methylprednisolone in epileptic encephalopathy: a randomised controlled trial

BackgroundHigh-level evidence for using steroids in epileptic encephalopathy (EE), other than West syndrome (WS), is lacking. This study investigated the efficacy and safety of pulse intravenous methylprednisolone (IVMP) in EE...

Seizure detection algorithm based on improved functional brain network structure feature extraction

Epilepsy is one of the most common neurological disorders. Accurate detection of epileptic seizures is essential for treatment. A seizure detection method with the structure of functional brain network, time, and frequency multi-domain features is presented in this paper. Pearson correlation coefficient (PCC) and mutual information (MI) can characterize the correlation between channels of EEG, and be used to construct PCC and MI combined functional brain networks (PMNet). Then the complex network theory is used to calculate the structure features of PMNet to explore whether it reflects seizure or not. After extraction of the multi-domain features, principal component analysis (PCA) is used to eliminate irrelevant feature information before feeding the features into the support vector machine (SVM) classification method. The method is tested on two open datasets (CHB-MIT and SIENA) and verified on the West syndrome (WS) dataset from the Children’s Hospital, Zhejiang University School of Medicine. The results demonstrate that the method based on the structure of functional brain networks and other multi-domain EEG features is competitive with other comparison methods. Furthermore, base on the method with brain network features, this paper has developed a simple automatic seizure detection system in hospital applications, which can reduce the workload of EEG technicians in reading EEG and marking seizures.

A-265 Neuropsychological Profile Associated with Psychogenic Nonepileptic Seizures, Autism, and Comorbid Neurodevelopmental Conditions

Abstract Objective: There is a dearth of data related to psychogenic nonepileptic seizures (PNES) in individuals with autism spectrum disorder (ASD). A neuropsychological case study was completed involving ASD and PNES to improve our understanding of brain behavior relationships and expand our clinical reach within this population. Method: A 9-year-old male with ASD, comorbid mild intellectual disability, attention-deficit hyperactivity disorder, and oppositional-defiant disorder was evaluated. He exhibited seizure-like episodes, involving staring, tremor, shaking, confusion, and prolonged memory lapses. Prior EEG was consistent with epileptiform focus in the right parasagittal region. Repeat EEG, brain MRI, and 3-day continuous EEG (cEEG) were unremarkable. Clinical episodes during cEEG had no EEG correlate or seizure episodes and included features inconsistent with epileptic seizures, indicating PNES. Results: Indices assessing aptitude and intellect were broadly dampened (&amp;lt; 1st-5th percentile), aside from visual-spatial processing (77th percentile). His profile revealed difficulties with language (e.g., receptive and expressive vocabulary, fluency, and comprehension), rote and contextual verbal learning and memory, executive functioning (e.g., working memory, inhibition, and switching), processing speed, and fine motor skills (all &amp;lt; 1st-9th percentile). Visual-perception and visual-motor integration were strengths. See Table 1 for selected neuropsychological results. Conclusions: Given combined neurodevelopmental diagnoses, neuropsychological challenges were expected. The visuospatial strength was consistent with literature findings that some with ASD display enhanced visual-perception. Because seizure episodes were identified as non-epileptic, while behaviorally concerning, these events were not likely contributing to the neurocognitive presentation. There is need for future studies to examine the neuropsychological profiles and brain-behavior relationships of individuals with PNES and ASD.

Perioperative electroencephalography in cardiac surgery with hypothermic circulatory arrest: a narrative review.

OBJECTIVESCardiac surgery with hypothermic circulatory arrest (HCA) is associated with neurological morbidity of variable severity and electroencephalography (EEG) is a sensitive proxy measure of brain injury. We conducted a narrative review of the literature to evaluate the role of perioperative EEG monitoring in cardiac surgery involving HCA.METHODSMedline, Embase, Central and LILACS databases were searched to identify studies utilizing perioperative EEG during surgery with HCA in all age groups, published since 1985 in any language. We aimed to compare EEG use with no use but due to the lack of comparative studies, we performed a narrative review of its utility. Two or more reviewers independently screened studies for eligibility and extracted data.RESULTSFourty single-centre studies with a total of 3287 patients undergoing surgery were identified. Most were observational cohort studies (34, 85%) with only 1 directly comparing EEG use with no use. EEG continuity (18, 45%), seizures (15, 38%) and electrocerebral inactivity prior to circulatory arrest (15, 38%) were used to detect, monitor, prevent and prognose neurological injury. Neurological dysfunction was reported in almost all studies and occurred in 0–21% of patients. However, the heterogeneity of reported clinical and EEG outcome measures prevented meta-analysis.CONCLUSIONSEEG is used to detect cortical ischaemia and seizures and predict neurological abnormalities and may guide intraoperative cerebral protection. However, there is a lack of comparative data demonstrating the benefit of perioperative EEG monitoring. Use of a standardized methodology for performing EEG and reporting outcome metrics would facilitate the conduct of high-quality clinical trials.

Astrocytic GABA transporter 1 deficit in novel SLC6A1 variants mediated epilepsy: Connected from protein destabilization to seizures in mice and humans

ObjectiveMutations in γ-aminobutyric acid (GABA) transporter 1 (GAT-1)-encoding SLC6A1 have been associated with myoclonic atonic epilepsy and other phenotypes. We determined the patho-mechanisms of the mutant GAT-1, in order to identify treatment targets. MethodsWe conducted whole-exome sequencing of patients with myoclonic atonic epilepsy (MAE) and characterized the seizure phenotypes and EEG patterns. We studied the protein stability and structural changes with homology modeling and machine learning tools. We characterized the function and trafficking of the mutant GAT-1 with 3H radioactive GABA uptake assay and confocal microscopy. We utilized different models including a knockin mouse and human astrocytes derived from induced pluripotent stem cells (iPSCs). We focused on astrocytes because of their direct impact of astrocytic GAT-1 in seizures. ResultsWe identified four novel SLC6A1 variants associated with MAE and 2 to 4 Hz spike-wave discharges as a common EEG feature. Machine learning tools predicted that the variant proteins are destabilized. The variant protein had reduced expression and reduced GABA uptake due to endoplasmic reticular retention. The consistent observation was made in cortical and thalamic astrocytes from variant-knockin mice and human iPSC-derived astrocytes. The Slc6a+/A288V mouse, representative of MAE, had increased 5–7 Hz spike-wave discharges and absence seizures. InterpretationSLC6A1 variants in various locations of the protein peptides can cause MAE with similar seizure phenotypes and EEG features. Reduced GABA uptake is due to decreased functional GAT-1, which, in thalamic astrocytes, could result in increased extracellular GABA accumulation and enhanced tonic inhibition, leading to seizures and abnormal EEGs.

Neonatal EEG seizure detection using a new signal structural complexity measure based on matching pursuit decomposition with nonstationary dictionary

Background and objectiveIn newborns, it is often difficult to accurately differentiate between seizure and non-seizure based solely on clinical manifestations. This highlights the importance of electroencephalogram (EEG) in the recognition and management of neonatal seizures. This paper proposes an effective algorithm for the detection of neonatal seizure using multichannel EEG. MethodsNeonatal EEG changes morphology as it alternates between seizure and non-seizure states. A new signal complexity measure based on matching pursuit (MP) decomposition is proposed and used to detect transitions between these two states. The new measure, referred to as weighted structural complexity (WSC), was used for the detection of seizures in 30 newborn EEG records. Multiple IIR filters and an MP-based filter were designed and used to remove artifacts from the EEG data. Geometrical correlation between the EEG data channels was applied to reduce the number of false detections caused by remnant artifacts. The seizure detector's performance was assessed using several epoch-based (e.g., accuracy) and event-based (GDR = good detection rate and FD/h = false detections per hour) metrics. ResultsCompared to the neurologist marking, the proposed detector was able to detect EEG seizures with 94% accuracy, 90.9% GDR, and 0.14 FD/h (95% CI: [0.06, 0.34]). ConclusionsThe high performance of the MP-based detector may have significant implications for the accurate diagnosis of neonatal seizures and the appropriate use of anticonvulsants and ongoing clinical assessment and care of the newborn.

Changes of oxygen saturation in patients with pure temporal lobe epilepsy

PurposeIctal hypoxemia is accepted as one of the mechanisms underlying sudden unexpected death in epilepsy (SUDEP). Although ictal hypoxemia is more common in generalized seizures, it also occurs in focal seizures with or without generalization. In this study, we aimed to show the relationship between clinical and electroencephalographic findings of seizures in patients with temporal lobe epilepsy (TLE) with periictal oxygen saturation. MethodsThe data of 55 adult patients who were hospitalized in the Video EEG Monitoring Unit (VEMU) and operated on for drug-resistant TLE between January 2017 and December 2020 were examined. Forty-five seizures from 21 patients with ictal peripheral arterial saturation information and that were seizure-free for at least a year during the follow-up were included in the study. ResultsThe median patient age was 28 (IQR 25–39.5) years (women: 9, men: 12). Age at epilepsy onset was negatively correlated with saturation at seizure onset. Moreover, the age at VEMU admission was also negatively correlated with saturation at seizure onset and the lowest levels of saturation. The saturation at the end of the seizures and the lowest saturation measured in the periictal period with generalization of EEG were significantly lower than those without generalization.The onset of ictal EEG with the rhythmic theta pattern was significantly associated with the lowest level of saturation (<90%), postictal generalized electroencephalographic suppression (PGES), and the presence of generalization. ConclusionAccording to the study, rhythmic ictal theta activity, older age, nocturnal seizure, and generalization in ictal EEG might increase the potential risk of SUDEP. Further studies including a greater number of subjects and different epilepsy syndromes may provide more comprehensive information about potential biomarkers for SUDEP.