Search

Background:Caring for women with epilepsy (WWE) during pregnancy poses unique challenges. We conducted an audit of the care our epilepsy clinic provided to pregnant WWE.Methods:We performed a retrospective study on all pregnancies followed by an epileptologist at a Canadian tertiary care centre’s epilepsy clinic between January 2003 and March 2021. Among 81 pregnancies in 53 patients, 72 pregnancies in 50 patients were analyzed to determine patient-related, follow-up-related, antiseizure-medication-related, and child-related pregnancy characteristics. Univariate analyses were performed to explore if these characteristics were associated with disabling seizure occurrence during pregnancy.Results:Most pregnancies were intended (72%) and occurred in women who used folic acid pre-pregnancy (76%) and who followed recommended blood tests for antiseizure medication (ASM) levels (71%). In 49% of pregnancies, ASM dosage was modified; 53% of these modifications were made in response to ASM blood levels. Most often used ASMs were lamotrigine (43%), followed by carbamazepine (32%) and levetiracetam (13%). One child was born with a thyroglossal duct cyst; our congenital malformation rate was thus 2%. Disabling seizures occurred in 24% of pregnancies. Exploratory analyses suggested that disabling seizure occurrence during pregnancy was associated with younger patient age (p = 0.018), higher number of ASMs used during pregnancy (p = 0.048), lamotrigine usage in polytherapy (p = 0.008), and disabling seizure occurrence pre-pregnancy (p = 0.027).Conclusion:This Canadian audit provides an in-depth description of pregnancies benefiting from specialized epilepsy care. Our results suggest an association between disabling seizure occurrence during pregnancy and lamotrigine usage in polytherapy that warrants further evaluation.

One-third of epilepsy patients suffer from medication-resistant seizures. While surgery to remove epileptogenic tissue helps some patients, 30–70% of patients continue to experience seizures following resection. Surgical outcomes may be improved with more accurate localization of epileptogenic tissue. We have previously developed novel thin-film, subdural electrode arrays with hundreds of microelectrodes over a 100–1,000 mm2 area to enable high-resolution mapping of neural activity. Here we used these high-density arrays to study microscale properties of human epileptiform activity. We performed intraoperative micro-electrocorticographic recordings within epileptic cortex (the site of seizure onset and early spread) in nine patients with epilepsy. In two of these patients, we obtained recordings from cortical areas distal to the epileptic cortex. Additionally, we recorded from two non-epileptic patients with movement disorders undergoing deep brain stimulator implantation as non-epileptic tissue controls. A board-certified epileptologist identified microseizures, which resembled electrographic seizures normally observed with clinical macroelectrodes. Epileptic cortex exhibited a significantly higher microseizure rate (2.01 events/min) than non-epileptic cortex (0.01 events/min; permutation test, P=0.0068). Using spatial averaging to simulate recordings from larger electrode contacts, we found that the number of detected microseizures decreased rapidly with increasing contact diameter and decreasing contact density. In cases in which microseizures were spatially distributed across multiple channels, the approximate onset region was identified. Our results suggest that micro-electrocorticographic electrode arrays with a high density of contacts and large coverage are essential for capturing microseizures in epilepsy patients and may be beneficial for localizing epileptogenic tissue to plan surgery or target brain stimulation.

This article is co-authored by a parent of a 32-year-old male patient with Lennox–Gastaut syndrome (LGS) and his epileptologist. It discusses the parent’s experience of having a child with LGS from diagnosis through living day-to-day with the disease and the physician’s perspective when treating this devastating epilepsy syndrome. The patient’s mother, who is his legal representative, provided written consent for publication of this article.

ObjectiveTo determine the incidence of first seizures, epilepsy, and seizure mimics in a geographically defined area using the updated 2014 International League Against Epilepsy (ILAE) definition, which allows an epilepsy diagnosis after a single seizure when the risk of further seizures over the next 10 years is ≈60% or greater. This replaced the 1993 definition by which epilepsy was diagnosed when a person had ≥2 seizures separated by 24 hours.MethodsUsing multiple overlapping methods of case ascertainment followed by individual case classification by an epileptologist, we identified all first seizures, new diagnosis of epilepsy, and seizure mimics occurring in a defined geographic area (population 542,868) from January 1, 2017, to December 31, 2017. Incidence was age standardized to the Standard European Population. We compared incidence rates using the 2014 and 1993 ILAE definitions.ResultsWhen the 2014 ILAE definition of epilepsy was applied, the incidence of new diagnosis of epilepsy was 62 per 100,000 (age standardized 74) compared to 41 per 100,000 (age standardized 48) when the 1993 definition was applied, and the difference was more pronounced at older ages. The incidence of all first seizures and of seizure mimics was 102 per 100,000 (age standardized 123) and 94 per 100,000 (age standardized 111), respectively. The most frequently encountered seizure mimic was syncope.ConclusionApplication of the 2014 ILAE definition of epilepsy resulted in a higher incidence of new diagnosis of epilepsy compared to the 1993 definition. The incidence of seizure mimics almost equals that of all first seizures. Seizures, epilepsy, and seizure mimics represent a significant burden to health care systems.

Infantile spasms remain the most challenging of the epileptic encephalopathies of childhood. Infantile spasms are classified as an epileptic encephalopathy, as the adverse cognitive and behavioral burden of the condition is out of proportion to the burden one would expect from the underlying etiology or the accompanying magnetic resonance imaging. The ictal and interictal electroencephalographic (EEG) activity is presumed to contribute to the progressive cerebral dysfunction. In many of these children, the underlying etiology also contributes to the severe mental subnormality and autistic behavior. Though it is the syndromic approach that guides the pediatric epileptologist, it is best to keep in mind that one syndrome may evolve into another in infancy and early childhood. A baby with Ohtahara syndrome may, after 2 to 7 months, begin to have spasms. Lennox-Gastaut syndrome with its typical seizure types and EEG may evolve in a child with infantile spasms.The unique modalities used in the treatment of infantile spasms make early recognition important. It is, however, also of paramount importance to make an etiological diagnosis as the underlying etiology may be eminently treatable. The treating physician cannot abandon them as wholly “intractable” epilepsy. The excellent response to treatment in the few who just cannot be defined or accurately predicted drives the physician to exercise his brain. Use of the two well-accepted modalities of treatment; vigabatrin and adrenocorticotrophic hormone singly or in combination, oral steroids in high dose, ketogenic diet, the conventional antiepileptic medications, and strategies to target the basic cause have been tried out by various clinicians. Here, we have made an attempt to collate evidence and describe the progress in the management of infantile spasms.

Objective Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings. Methods An epileptologist annotated 13,959 epileptiform discharges from a dataset of intracranial EEG recordings from 46 epilepsy patients. Using this dataset, we trained a convolutional neural network (CNN) to recognize mTL epileptiform discharges from a single intracranial bipolar channel. The CNN outputs from multiple bipolar channel inputs were averaged to generate the final detector output. Algorithm performance was estimated using a nested 5-fold cross-validation. Results On the receiver-operating characteristic curve, our algorithm achieved an area under the curve (AUC) of 0.996 and a partial AUC (for specificity > 0.9) of 0.981. AUC on a precision-recall curve was 0.807. A sensitivity of 84% was attained at a false positive rate of 1 per minute. 35.9% of the false positive detections corresponded to epileptiform discharges that were missed during expert annotation. Conclusions Using deep learning, we developed a high-performing, patient non-specific algorithm for detection of mTL epileptiform discharges on intracranial electrodes. Significance Our algorithm has many potential applications for understanding the impact of mTL epileptiform discharges in epilepsy and on cognition, and for developing therapies to specifically reduce mTL epileptiform activity.

Introduction . Epilepsy is a neurological disorder characterized by periodic seizure attacks. Around 70–80% of epilepsy cases have a hereditary component. Aim : to identify the genetic factors of pharmacoresistant epilepsy in children. Materials and methods . Fifty two patients with epilepsy and psychomotor / speech retardation were examined. We used the next generation sequencing (NGS) technique, which is the targeted exome sequencing, the “Hereditary epilepsy” panel of genes, and the whole exome sequencing assay. Results . Mutations were detected in 30 (57.7%) patients, while 22 patients had no mutations. In the latter cases, either epilepsy was of non-hereditary nature or the tested nucleotide sequence was located in the non-coding part of the gene (intron); in addition, a chromosomal rearrangement could be involved. Conclusion . The obtained data illustrate a diagnostic significance of the whole exome sequencing and encourage the interaction between an epileptologist and a geneticist in the diagnostic procedure. Identification of the genetic base of the disease is of great importance for genetic counseling and for selecting an antiepileptic therapy in this group of patients. The authors declare about the absence of conflict of interest with respect to this publication. Authors contributed equally to this article.

Sturge-Weber-Crabbe syndrome (SWCS, encephalo-trigeminal angiomatosis) is a rare neurodermal disease (phacomatosis), characterized by vascular skin, eyes and brain abnormalities. The incidence is 1 for 50,000 and it is equal in women and men. All races and ethnic groups are affected without gender differences. The history of the first descriptions of SWCS, its detailing, elements of etiopathogenesis and clinical description, aspects of the medical imaging technologies application in SWCS identifying are presented. As part of a clinical observation the brain CT and MRI results of the 9-year patient with SWCS are described and illustrated in details. It is noted that patients with SWCS should be under the supervision of a multidisciplinary team (medical geneticist, dermatologist, neurologist, epileptologist, neurosurgeon, neuropsychologist, ophthalmologist, ophthalmosurgeon, vascular surgeon, neuroradiologist) with training in the diagnostics and treatment of hereditary neurodermal syndromes . Keywords: Sturge-Weber-Crabbe syndrome, encephalo-trigeminal angiomatosis, phacomatosis.

Approximately 50 million people have epilepsy worldwide. Prognosis may vary among patients depending on their seizure semiology, age of onset, seizure onset location, and features of electroencephalogram (EEG). Several researchers have focused on EEG patterns and demonstrated that EEG patterns of individuals with epilepsy can be used to predict prognosis and treatment responses. However, accurate EEG analysis requires an experienced epileptologist with several years of training, who are often unavailable in small or medium sized hospitals. In this paper, a novel machine learning (ML) model that accurately distinguishes Benign Epilepsy with Centrotemporal Spikes (BECTS) from Temporal Lobe Epilepsy (TLE) is proposed. BECTS and TLE show different seizure types and age of onset, but differential diagnosis can be challenging due to the similar location and patterns of the EEG spikes. The proposed hybrid machine learning (HML) model processes the diagnosis in the order of (1) creating feature matrices using statistical indexes after signal decomposition, (2) processing feature selection using Support Vector Machine (SVM) technology, and (3) classifying the results through ensemble learning based on decision trees. Simulation was performed using real patient data of 112 BECTS and 112 TLE EEG signals, where training was performed using 80% of the data and 20% of the data was used in the performance analysis comparison with the actual labeled data based on the diagnosis of medical doctors. The performance of the hybrid classification model is compared with other representative ML algorithms, which include logistic regression, KNN, SVM, and ensemble learning based decision tree. The model proposed in this paper shows an accuracy performance exceeding 99%, which is higher than the performance obtainable from the other ML classification models. The purpose of this study is to introduce a novel EEG diagnostic system that shows maximum efficiency to support clinical real-time diagnosis that can accurately distinguish epilepsy types. Future research will focus on expanding this ML model to categorize other types of epilepsies beyond BECTS and TLE and implement the HML diagnostic blockchain database into the hospital system.

EEG source imaging is becoming widely used for the evaluation of medically refractory focal epilepsy. The validity of EEG source imaging has been established in several studies comparing source imaging to the surgical resection cavity and subsequent seizure freedom. We present a cohort of 87 patients and compare EEG source imaging of both ictal and interictal scalp EEG to the seizure onset zone on intracranial EEG. Concordance of EEG source imaging with intracranial EEG was determined on a sublobar level and was quantified by measuring the distance between the source imaging result and the centroid of the active seizure onset zone electrodes. The EEG source imaging results of a subgroup of 26 patients with high density 76-channel EEG were compared with the localization of three experienced epileptologists. Of 87 patients, 95% had at least one analysis concordant with intracranial EEG and 74% had complete concordance. There was a higher rate of complete concordance in temporal lobe epilepsy compared to extratemporal (89.3 and 62.8%, respectively, P = 0.015). Of the total 282 analyses performed on this cohort, higher concordance was also seen in temporal discharges (95%) compared to extratemporal (77%) (P = 0.0012), but no difference was seen comparing high-density EEG with standard (32-channel) EEG. Subgroup analysis of ictal waveforms showed greater concordance for ictal spiking, compared with rhythmic activity, paroxysmal fast activity, or obscured onset. Median distances from the dipole and maximum distributed source to a centroid of seizure onset zone electrodes were 30.0 and 32.5 mm, respectively, and the median distances from dipole and maximum distributed source to nearest seizure onset zone electrode were 22.8 and 21.7, respectively. There were significantly shorter distances in ictal spiking. There were shorter distances in patients with Engel Class 1 outcome from surgical resection compared to patients with worse outcomes. For the subgroup of 26 high-density EEG patients, EEG source localization had a significantly higher concordance (92% versus 65%), sensitivity (57% versus 35%) and positive predictive value (60% versus 36%) compared with epileptologist localization. Our study demonstrates good concordance between ictal and interictal source imaging and intracranial EEG. Temporal lobe discharges have higher concordance rates than extratemporal discharges. Importantly, this study shows that source imaging has greater agreement with intracranial EEG than visual review alone, supporting its role in surgical planning., EEG source imaging of 87 patients with focal epilepsy demonstrated overall concordance of ictal and interictal discharges with intracranial EEG. Improved concordance was seen with temporal discharges and with ictal spiking compared to other waveforms. Source imaging demonstrated improved concordance compared to epileptologist review in a subgroup of high-density EEG., Graphical Abstract Graphical Abstract

Nonepileptic seizures are commonly associated with psychiatric comorbidities, and specifically PTSD. Despite increased prevalence of psychiatric disease noted on referral of patients to our dedicated clinic for nonepileptic seizures, we found even higher rates of comorbid psychiatric disease or significant symptomatology after our initial clinic intakes, whereby patients are formally evaluated by a behavioral health provider, in addition to an epileptologist. After intake, an additional 21% of patients were identified as having PTSD or significant trauma-related symptoms, an additional 7% of patients were identified with significant anxiety or panic-related symptoms, and an additional 11% of patients were identified with significant depressive symptoms. While highly effective treatment of nonepileptic seizures remains elusive, well-developed treatment paradigms with proven efficacy exist for depression, anxiety, and PTSD. Eliciting these psychiatric comorbidities and pursuing targeted treatments, especially for those patients that do not have easy access to providers with dedicated expertise in the management of nonepileptic seizures, may be a more easily scalable and implementable treatment modality for these patients.

Epilepsy is a major neurological disorder affecting approximately 1% of the world population, where seizure semiology is an essential tool for clinical evaluation of seizures. This includes qualitative visual inspection of videos from the seizures in epilepsy monitoring units by epileptologists. In order to support this clinical diagnosis process, promising deep learning-based systems were proposed. However, these indicate that video datasets of epileptic seizures are still rare and limited in size. In order to enable the full potential of AI systems for epileptic seizure diagnosis support and research, a novel collaborative development framework is proposed for a scalable DL-assisted clinical research and diagnosis support of epileptic seizures. The designed cloud-based approach integrates our deployed and tested NeuroKinect data acquisition pipeline into an MLOps framework to scale data set extension and analysis to a multi-clinical utilization. The proposed development framework incorporates an MLOps approach, to ensure convenient collaboration between clinicians and data scientists, providing continuous advantages to both user groups. It addresses methods for efficient utilization of HW, SW and human resources. In the future, the system is going to be expanded with several AI-based tools. Such as DL-based automated 3D motion capture (MoCap), 3D movement analysis support, quantitative seizure semiology analysis tools, video-based MOI and seizure classification.

Due to the coronavirus disease 2019 (COVID-19) pandemic, the state of Texas-limited elective procedures to conserve beds and personal protective equipment (PPE); therefore, between March 22 and May 18, 2020, admission to the epilepsy monitoring unit (EMU) was limited only to urgent and emergent cases. We evaluated clinical characteristics and outcomes of these patients who were admitted to the EMU. Nineteen patients were admitted (one patient twice) with average age of 36.26 years (11 female) and average length of stay 3 days (range: 2-9 days). At least one event was captured on continuous EEG (cEEG) and video monitoring in all 20 admissions (atypical in one). One patient had both epileptic (ES) and psychogenic non-epileptic seizures (PNES) while 10 had PNES and 9 had ES. In 8 of 9 patients with ES, medications were changed, while in 5 patients with PNES, anti-epileptic drugs (AED) were stopped; the remaining 5 were not on medications. Of the 14 patients who had seen an epileptologist pre-admission, 13 (or 93%) had their diagnosis confirmed by EMU stay; a statistically significant finding. While typically an elective admission, in the setting of the COVID-19 pandemic, urgent and emergent EMU admissions were required for increased seizure or event frequency. In the vast majority of patients (13 of 19), admission lead to medication changes to either better control seizures or to change therapeutics as appropriate when PNES was identified.

Proper documentation during neurology clinic visits is essential. Medical providers that document precise and standardized findings aid other providers, nurses, and research personnel. The American Academy of Neurology (AAN) created standardized quality measures in order to improve delivery of care for patients with epilepsy, providers, and systems. In this article, we reviewed a total of 777 charts in order to find documentation on seizure frequency from the most recent clinical visit. Data was collected from electronic healthcare records. During initial chart review, the following information was noted: age, gender, seizure type(s), etiology type(s), provider (neurologist or epileptologist), whether seizure frequency was noted, and the reason for no documentation. The data review represented a sample of the epilepsy population seen at our institution. Of the 734 individuals, 475 patients had seizure frequency documented (65%). Two hundred and fifty-nine (259) people (35%) were missing seizure frequency data. For those individuals, we determined the reasoning behind why this data was not present in the chart note. Of those 259 charts, there were 65 (25%) charts missing seizure frequency, 161 (62%) charts that were vague, and 33 (13%) charts where seizure frequency could not be determined. Based on our findings, the documentation of seizure frequency is a gap in care.

Drug-refractory epilepsy is a commonly prevalent pediatric neurological illness of global significance. Ketogenic diet (KD) is a time-tested therapeutic modality for refractory epilepsy, which has reemerged as a robust alternative to anti-epileptic pharmacotherapy. There is a growing body of evidence which supports the anti-seizure efficacy, safety profile and feasibility of KD use in childhood epilepsy. In addition, this modality has been recognized to reduce anti-epileptic exposure, improve cognition and behavioral profile of patients as well as improve the quality-of-life of care-givers. Current indications of KD include refractory epilepsy syndromes, selected metabolic disorders (such as pyruvate dehydrogenase deficiency) and a host of varied neurological entities. KD research has broadened the knowledge-base about its mechanisms of action. Four types of KD are in vogue currently with varying nutritional constitution, palatability, administration protocols and comparable efficacy. KD initiation and maintenance are the result of concerted effort of a team of pediatric neurologist/epileptologist, nutritionist and patient's primary care-giver. Consensus is being formulated about various practical aspects of KD such as patient-selection, parental counseling, baseline work-up, dietary prescription, nutritional supplementation, concurrent anti-epileptic drug administration, follow-up and treatment-duration. Novel applications of KD include its use in neonatal epilepsy and super-refractory status epilepticus and tailor-made formulations such as cooking oil-based KD in predominantly rice-fed populations. Increasing body of clinical experience, improved nutritional designs and translational research are promoting KD as a major therapeutic modality. Currently, KD forms a core essence in the armamentarium against refractory epilepsy. In this review, we summarize the recent advances and current perspectives in the use of KD in refractory epilepsy.

We present the methodology and results of participatory design of a robot for presenting an epileptic seizure and a scenario of the educational workshop using this robot. Children with epilepsy encounter stigma and stereotypes and may receive inadequate aid when having an epileptic seizure. The goal of the larger project was to use the prototype device in a series of workshops for improving teachers' actions during an epileptic seizure and their attitudes towards epileptic students. In this paper, we show how various design goals for an educational robot were accomplished to fit the needs of all identified stakeholders, particularly people with epilepsy.We used a co-design (participatory design) approach through a series of meetings participated by members of the association Polish Association for People Suffering from Epilepsy, students and faculty members of the biomedical engineering and robotics departments, teachers, psychologists and medical specialists (epileptologist, neurologist).These me...

Background.It has been suggested that the part of the cryptogenic epilepsies is a consequence of minor-foci disorders of cortical architectonics, the diagnosis of which is not always possible due to the unavailability of MR-scanners with high magnetic induction.Objective:determination of the best options of the low-field MRI-device for visualization of epileptogenic brain malformations in children with symptomatic focal forms of epilepsy.Materials and methods.Were analyzed MRI data of 24 children undergoing investigations regarding for difficult-to-treat or pharmacoresistant forms of focal epilepsy in the Department of Magnetic-Resonance Tomography, Central Children Clinical Hospital of FMB Agency of Russia at 2015–2017. All the patients underwent brain MRI according to standard routine protocol. Simultaneously we review conclusions of epileptologist and the preliminary video-electroencefalographic monitoring data for determination of the optimal imaging protocol for every specific form of epilepsy. For imaging of the epileptogenic brain lesion the MRI study was conducted on open-ended device “Aperto” (Hitachi Ltd., Japan) of static magnetic field with the tension characteristics of 0.4 T. The thickness of the slices and the scan step was performed at 3.0 and 3.5 mm (the maximum value of slice thickness and step due to the technical conditions of the used scanner without losing in signalto-noise ratio) with the use of special positioning of slices in the coronal and axial projections, T2, T1, STIR, FLAIR weighted images perpendicular and parallel to the long axis of the hippocampus.Results and conclusion.In 24 patients were revealed structural brain changes that have neuroradiological signs of brain malformations. In all the patients this changes were associated with difficult to treat and drug-resistant forms of focal epilepsy. The newly identified malformations were observed in 10 patients, and in 3 cases the changes detected after previous MRI (including high-field MRI-devices) whose results were false-negative. In 11 patients diffuse brain abnormalities had been revealed, including the combinations of several hypogenesis and dysplastic pathologies. In 13 patients were marked different types of hemispheric and regional disorders of cortical development including focal cortical dysplasia. Extensive unilateral and bilateral changes were clearly distinguishable on the routine MRI. The low-tension technique approximated to the epileptic scanning protocol in some cases allowed to assess the affected area and revealed the combination of different variants of pathological cortical organization. In 7 cases the preliminary diagnosis based on the results of previous MRI studies including high-field MRI-devices. In 2 of these patients this changes were minor-focal, not visualized according to the routine MRI protocol, and had the differentiation characteristics between focal cortical dysplasia IIb/dysembryoplastic neuroepithelial tumor/ganglioglioma types. Disappointing results of visualization of mesial-basal temporal lobe regions aimed to detect small-caliber intracortical formations were observed. These patients contained a separate group of 12 children for whom extensive investigation which includes high-field MRI scan protocol on epileptic program was recommended.

Objective Abnormal electroencephalography (EEG) patterns are common after resuscitation from cardiac arrest and have clinical and prognostic importance. Bedside continuous EEGs are not available in many institutions. We tested the feasibility of using a point-of-care system for EEG acquisition. Methods We prospectively enrolled a convenience sample of post-cardiac arrest patients between 9/2015–1/2017. Upon hospital arrival, a limited EEG montage was applied. We tested both continuous EEG (cEEG) and this point-of-care EEG (eEEG). A board-certified epileptologist and a board-certified neurointensivist jointly reviewed all EEGs. Cohen’s kappa coefficient evaluated agreement between eEEG and cEEG and Fisher’s exact test evaluated their associations with survival to hospital discharge and proximate cause of death. Results We studied 95 comatose post-cardiac arrest patients. Mean age was 59 (SD17) years. Most (61%) were male, few (N = 22; 23%) demonstrated shockable rhythms, and PCAC IV illness severity was present in 58 (61%). eEEG was interpretable in 57 (60%) subjects. The most common eEEG interpretations were: continuous (21%), generalized suppression (14%), burst-suppression (12%) and burst-suppression with identical bursts (10%). Seizures were detected in 2 eEEG subjects (2%). No patient with seizure or burst-suppression with identical bursts survived. cEEG demonstrated generalized suppression (31%), burst-suppression with identical bursts (27%), continuous (18%) and seizure (4%). The eEEG and cEEG demonstrated fair agreement (kappa = 0.27). Neither eEEG nor cEEG was associated with survival (p = 0.19; p = 0.11) or proximate cause of death (p = 0.14; p = 0.8) Conclusions eEEG is feasible, although artifact often precludes interpretation. eEEG is fairly associated with cEEG and may facilitate post-cardiac arrest care.

If the deceased world-famous boxer, Muhammad Ali, was an epileptologist, he would have described the EEG tracing we are presenting in this 43rd edition of the Waveform Window, “As focal as can be a...

Aims We aimed to classify ictal onset patterns (IOPs) in pediatric patients undergoing intracranial electroencephalography (IEEG) to guide surgery for refractory epilepsy. We aimed to determine if morphology of IOPs can predict surgical outcome. Materials and Methods We performed a retrospective review of pediatric patients who underwent epilepsy surgery guided by subdural IEEG from 2007 to 2016. IEEG seizures were reviewed by a blinded epileptologist. Data was collected on outcomes. Results Twenty-three patients with 784 seizures were included. Age at seizure onset was 0.2–11 (mean 4.3, standard deviation 3.2) years. Age at time of IEEG was 4–20 (mean 13.5, standard deviation 4.4) years. Five distinct IOPs were seen at seizure onset: A) Low voltage fast activity (LVFA) with spread to adjacent electrodes (n = 7 patients, 30%), B) Burst of LVFA followed by electrodecrement (n = 12 patients, 52%), C) Burst of rhythmic spike waves (RSW) followed by electrodecrement (n = 9 patients, 39%), D) RSW followed by LVFA (n = 7 patients, 30%), E) Rhythmic spikes alone (n = 10 patients, 43%). Twelve patients (52%) had the same IOP type with all seizures. When the area of the IOP was resected, 14 patients (61%) had Engel I outcomes. Patients who had LVFA seen within their predominant IOP type were more likely to have good surgical outcomes (odds ratio 7.50, 95% confidence interval 1.02–55.0, p = 0.05). Patients who had only one IOP type were more likely to have good outcomes than patients who had multiple IOP types (odds ratio 12.6, 95% confidence interval 1.19–134, p = 0.04). Patients who had LVFA in their predominant IOP type were older than patients who did not have LVFA (mean age 15.0 vs. 9.9 years, p = 0.02). Conclusions LVFA at ictal onset and all seizures having the same IOP morphology are associated with increased likelihood of surgical success in children, but LVFA is less common in children who are younger at the time of IEEG.

Introduction : A single-lead electrocardiogram (EKG) is routinely recorded with electroencephalogram (EEG). This study investigates the frequency and types of EKG abnormalities during routine EEG. Methods : All routine EEGs (20–60 min) over one year were retrospectively analyzed. A blinded cardiologist interpreted EKG recordings. An epileptologist evaluated EEGs. Demographic data, underlying comorbidities, and indications for the EEG were extracted. Results : A total of 433 recordings for 365 patients were included. Mean (±SD) age was 46.8 (±21.3) years and 50.4% were females. EKG abnormalities were detected in 28.5% of patients; sinus tachycardia (11%), premature ventricular contractions (7.9%), atrial fibrillation (Afib) (6.3%), sinus bradycardia (2.2%) and premature atrial contractions (1.1%). Afib was more common in females than males (p = 0.020), confirmed in six out of seven patients and discovered in 17 patients. Age (OR: 1.67, 95%CI: 1.05–2.66, p = 0.031), prior diagnosis of epilepsy (OR: 2.25, 95% CI: 1.22 – 4.14, p = 0.009), history of seizure (OR: 1.97, 95%CI: 1.09–3.54, p = 0.024), abnormal EEG (OR: 2.14, 95%CI: 1.25 – 3.66, p = 0.005) and EEGs evaluating seizures/epilepsy (OR: 4.18, 95% CI: 1.32 – 13.21, p = 0.015) or syncope (OR: 3.21, 95% CI: 1.16 – 8.84, p = 0.024) were independently associated with abnormal EKG. Conclusion : The frequency of EKG abnormalities captured during routine EEGs was high, with Afib being the most significant. Older age, history of epilepsy or seizure, abnormal EEGs, and EEGs evaluating seizures/epilepsy or syncope were significant predictors. These findings suggest neurologists to become more vigilant to EKG recorded during routine EEG as such findings might have diagnostic and therapeutic implications.

Objective Scalp electroencephalographic (EEG)-functional magnetic resonance imaging (fMRI) studies suggest that the maximum blood oxygen level-dependent (BOLD) response to an interictal epileptiform discharge (IED) identifies the area of IED generation. However, the maximum BOLD response has also been reported in distant, seemingly irrelevant areas. Given the poor postoperative outcomes associated with extra-temporal lobe epilepsy, we hypothesized this finding is more common when analyzing extratemporal IEDs as compared to temporal IEDs. We further hypothesized that a subjective, holistic assessment of other significant BOLD clusters to identify the most clinically relevant cluster could be used to overcome this limitation and therefore better identify the likely origin of an IED. Specifically, we also considered the second maximum cluster and the cluster closest to the electrode contacts where the IED was observed. Methods Maps of significant IED-related BOLD activation were generated for 48 different IEDs recorded from 33 patients who underwent intracranial EEG-fMRI. The locations of the maximum, second maximum, and closest clusters were identified for each IED. An epileptologist, blinded to these cluster assignments, selected the most clinically relevant BOLD cluster, taking into account all available clinical information. The distances between these BOLD clusters and their corresponding IEDs were then measured. Results The most clinically relevant cluster was the maximum cluster for 56% (27/48) of IEDs, the second maximum cluster for 13% (6/48) of IEDs, and the closest cluster for 31% (15/48) of IEDs. The maximum clusters were closer to IED contacts for temporal than for extratemporal IEDs (p = .022), whereas the most clinically relevant clusters were not significantly different (p = .056). Significance The maximum BOLD response to IEDs may not always be the most indicative of IED origin. We propose that available clinical information should be used in conjunction with EEG-fMRI data to identify a BOLD cluster representative of the IED origin.

Objective Epilepsy is a neurologic disease that carries a high disease burden and likely, a huge treatment gap especially in low-to-middle income countries (LMIC) such as the Philippines. This review aimed to examine the treatment gaps and challenges that burden Philippine epilepsy care. Materials & methods Pertinent data on epidemiology, research, health financing and health systems, pharmacologic and surgical treatment options, cost of care, and workforce were obtained through a literature search and review of relevant Philippine government websites. Results The estimated prevalence of epilepsy in the Philippines is 0.9%. Epilepsy research in the Philippines is low in quantity compared with the rest of Southeast Asia (SEA). Inequities in quality and quantity of healthcare services delivered to local government units (LGUs) have arisen because of devolution. Programs for epilepsy care by both government and nongovernment institutions have been implemented. Healthcare expenditure in the Philippines is still largely out-of-pocket, with only partial coverage from the public sector. There is limited access to antiseizure medications (ASMs), mainly due to cost. Epilepsy surgery is an underutilized treatment option. There are only 20 epileptologists in the Philippines, with one epileptologist for every 45,000 patients with epilepsy. In addition, epilepsy care service delivery has been further impeded by the coronavirus disease of 2019 (COVID-19) pandemic. Conclusion There is a large treatment gap in epilepsy care in the Philippines in terms of high epilepsy disease burden, socioeconomic limitations and inadequate public support, sparse clinico-epidemiologic research on epilepsy, inaccessibility of health care services and essential pharmacotherapy, underutilization of surgical options, and lack of specialists capable of rendering epilepsy care. Acknowledgment of the existence of these treatment gaps and addressing such are expected to improve the overall survival and quality of life of patients with epilepsy in the Philippines.

Purpose This study evaluated the sensitivity of nonconvulsive seizure detection by non-neurophysiologist physicians and nurses using a panel of quantitative EEG (QEEG) trends in the setting of a pediatric intensive care unit. Methods Forty-five 1-hour QEEG epochs were obtained retrospectively from 10 patients admitted to the McMaster Children's Hospital pediatric intensive care unit, which included 184 electrographic seizures. Each epoch constituted 4 QEEG trends, a seizure probability marker, automated seizure detector, rhythmicity spectrograms, and amplitude-integrated EEG. Six pediatric residents and 5 pediatric intensive care unit nurses analyzed the epochs for possible seizures after a 15-minute power point presentation. This was compared with the gold standard of a board-certified epileptologist interpreting the conventional EEG data for seizures. Results Sensitivity of seizure detection for pediatric residents and intensive care unit nurses were 0.90. The specificity was 0.87 and 0.89, respectively. The interrater agreement among the pediatric residents was moderate with a kappa (κ) value of 0.45 (confidence interval: 0.41-0.49), and among the nurses were moderate with a κ value of 0.59 (confidence interval: 0.54-0.63). A post hoc analysis involving 2 neurophysiologists demonstrated a sensitivity of 0.90 and a specificity of 0.93 (confidence interval: 0.90-0.96) for seizure detection and a substantial interrater agreement of κ = 0.76 (confidence interval: 0.61-0.91). Conclusions A panel of QEEG trends can be used by non-neurophysiologists in a pediatric critical care setting to detect nonconvulsive seizures with a reasonable accuracy, which may expedite subclinical seizure identification and timely intervention.

Highlights • Novel coronavirus disease 2019 (COVID-19) complicates sedation guidance. • Limited frontotemporal encephalogram (EEG) guided sedation in COVID-19 patients. • A quantitative EEG machine learning algorithm classified patients’ sedation depth., Objective To study if limited frontotemporal electroencephalogram (EEG) can guide sedation changes in highly infectious novel coronavirus disease 2019 (COVID-19) patients receiving neuromuscular blocking agent. Methods 98 days of continuous frontotemporal EEG from 11 consecutive patients was evaluated daily by an epileptologist to recommend reduction or maintenance of the sedative level. We evaluated the need to increase sedation in the 6 hours following this recommendation. Post-hoc analysis of the quantitative EEG was correlated with the level of sedation using a machine learning algorithm. Results Eleven patients were studied for a total of ninety-eight sedation days. EEG was consistent with excessive sedation on 57 (58%) and adequate sedation on 41 days (42%). Recommendations were followed by the team on 59% (N=58; 19 to reduce and 39 to keep the sedation level). In the 6 hours following reduction in sedation, increases of sedation were needed in 7 (12%). Automatized classification of EEG sedation levels reached 80% (±17%) accuracy. Conclusions Visual inspection of a limited EEG helped sedation depth guidance. In a secondary analysis, our data supported that this determination may be automated using quantitative EEG analysis. Significance Our results support the use of frontotemporal EEG for guiding sedation in patients with COVID-19.

Background Various strategies have been proposed for the treatment of gelastic seizures due to hypothalamic hamartomas (HH), advancing from surgical removal techniques toward functional disconnection strategies. One of the most recent procedure is the Magnetic Resonance guided Laser Interstitial Thermal Therapy (MRg-LITT), which has progressively proved to be a safe and effective technique for hamartomas ablation. In this paper, the authors' preliminary experience with the first two patients treated with this technique in Italy is presented, in order to underline the feasibility of a pure non robotized frameless technique (FS MRg-LITT) while confirming the procedure effectiveness on seizure control. Methods Patients undergoing FS MRg-LITT for the treatment of HH related gelastic seizures since January 2020 were included. A two steps procedure was performed by using the neuronavigation system to define the entry point, the trajectories and to assess the accuracy. Visualase Laser Ablation System was then used for the MR guided ablation of the HH. A multidisciplinary (neurosurgeons, epileptologist, neuroradiologist) institutional board evaluated the patients both in the perioperative period and during follow up. Results A total number of 2 pediatric patients were described. The mean operative time resulted to be 6 hours while the mean accuracy was 0.4 mm. No peri-operative complications were reported. The mean length of stay was 4 days. Lastly, at 1-year follow up both patients resulted to be seizure free and endocrinological functions were preserved. Conclusions FS MRg-LITT for the treatment of HH-related epilepsy could represent an effective technique, being able to guarantee adequate level of accuracy and potentially extending the accessibility of MRg-LITT by lowering its costs and simplifying the overall procedure.

In this proof-of-concept investigation, we demonstrate a marker-free video-based method to detect nocturnal motor seizures across a spectrum of motor seizure types, in a nighttime setting with a single adult female with refractory epilepsy. In doing so, we further explore the intermediate biosignals, visually mapping seizure “fingerprints” to seizure types. The method is designed to be flexible enough to generalize to unseen data, and shows promising performance characteristics for low-cost seizure detection and classification. The dataset contained recordings from 27 recorded nights. Seizure events were observed in 22 of these nights, with 36 unequivocally confirmed seizures. Each seizure was classified by an expert epileptologist according to both the ILAE 2017 standard and the Lüders semiological classification guidelines, yielding 5 of the ILAE-recognized seizure types and 7 distinct seizure semiologies. Evaluation was based on inference of motion, oscillation, and sound signals extracted from the recordings. The model architecture consisted of two feature extraction and event determination layers and one thresholding layer, establishing a simple framework for multimodal seizure analysis. Training of the optimal parameters was done by randomly resampling the event hits for each signal, and choosing a threshold that kept an expected 90 % sensitivity for the sample distribution. With the cut-off values selected, statistical performance was calculated for two target seizure groups: those containing a clonic component, and those containing a tonic component. When tuned to 90 % sensitivity, the system achieved a very low false discovery rate of 0.038/hour when targeting seizures with a clonic component, and a clinically-relevant rate of 1.02/hour when targeting seizures with a tonic component. These results indicate a sensitive method for detecting various nocturnal motor seizure types, and a high potential to differentiate motor seizures based on their video and audio signal characteristics. Paired with the low cost of this technique, both cost savings and improved quality of care might be achieved through further development and commercialization of this method. publishedVersion

We aimed to describe perspectives of transition and transfer of adolescents and young adults with childhood-onset epilepsy from pediatric to adult care from the viewpoints of both pediatric and adult epileptologists. Telephone semi-structured interviews with pediatric (n = 15) and adult (n = 11) epileptologists at leading U.S. epilepsy centers were used to collect data about the transition process. Interviews were audio-recorded, transcribed, systematically coded using thematic analysis by two independent researchers, and subsequently checked for agreement during regular meetings. Participants were on average 46 years old (SD = 7.4), 50% male, 91% Non-Hispanic and 85% Caucasian; all had completed a formal epilepsy or clinical neurophysiology fellowship (mean = 11 years since terminal training) and were employed at a comprehensive epilepsy center. Three interrelated themes regarding epileptologists’ perspectives of epilepsy transition and transfer of care were evident: (1) the process is unnatural and disruptive, (2) clinicians make the best of challenges, and (3) the epilepsy transition process includes a spectrum of broad needs some of which are unique to epilepsy care while others are common to other chronic diseases. Despite challenges, epilepsy clinicians spontaneously expressed stress, empathy, and commitment to providing the best possible care.

A First Seizure/New Onset Epilepsy (FS/NOE) protocol was implemented to ensure proper evaluation by an epileptologist and improve overall care for patients. We compared healthcare utilization and cost incurred by patients pre and post protocol implementation.Clinical data were retrospectively collected from the EMR and cost data from the financial database. Patients were identified by FS event and grouped into either the pre-implementation (pre-FSC) or post-implementation cohort (post-FSC). Pre-FSC patients were seen between January 2014-December 2015 and post-FSC between March 2016-January 2018. Utilization outcomes include time from FS to neurology appointment, MRI, and electroencephalogram (EEG). Cost outcomes included the annualized median difference in pre versus post costs for ER, inpatient, outpatient or ambulatory, and total hospital services. Cost and utilization outcomes were collected within 90 days or 6 months post first-seizure event. Pre and post cohorts were compared using Kaplan-Meier analysis and Cox proportional hazard models for time-to-event outcomes, multivariable median regression models for cost differences and negative binomial regression models for utilization analyses. Models were adjusted for age, sex, health insurance, and comorbidities.One-hundred and fifty six patients were included with 84 (53.8%) pre- and 72 (46.2%) post-FSC patients. Kaplan-Meier and Cox regression results indicated post-FSC patients had significantly faster time-to-first neurology appointment (5.0 vs. 20.9 days, p .001; Adjusted Hazard Ratio (HR) = 5.98, p .001), time-to-MRI (9.0 vs. 27.0 days; p = 0.005; HR = 1.88, p = .021) and EEG (3.6 vs. 48.6 days, p .001; HR = 9.01, p .001). A total of 138 patients had at least one cost in the financial database. For 6-month follow-up period, post-FSC patients had higher adjusted all-cause total median costs (+$830, p = 0.009) and outpatient costs (+$1203, p .001) but lower ED costs (-245, p = 0.073), not significant. Results were similar for seizure-related costs. Similarly, Post-FSC patients had a significantly higher likelihood of all-cause (Adjusted Rate Ratio (ARR) = 1.41, p = .029) and outpatient utilization (ARR = 1.72, p = .008) but lower ED utilization (ARR = 0.54, p .001).Implementation of the FSC decreased time to evaluation by a neurologist and time to diagnostic workup. Ultimately, total healthcare costs and ambulatory costs increased but ED costs and utilization were reduced. It is our hypothesis that faster access to initial care and diagnosis would result in better control of seizures and reduce long-term costs and utilization. Further research over a longer duration of time across a broader population is needed to evaluate the full implications of an epilepsy specialist-populated FSC.

We compared sudden unexpected death in epilepsy (SUDEP) diagnosis rates between North American SUDEP Registry (NASR) epileptologists and original death investigators, to determine degree and causes of discordance. In 220 SUDEP cases with post-mortem examination, we recorded the epileptologist adjudications and medical examiner- and coroner- (ME/C) listed causes of death (CODs). COD diagnosis concordance decreased with NASR's uncertainty in the SUDEP diagnosis: highest for Definite SUDEP (84%, n = 158), lower in Definite Plus (50%, n = 36), and lowest in Possible (0%, n = 18). Rates of psychiatric comorbidity, substance abuse, and toxicology findings for drugs of abuse were all higher in discordant cases than concordant cases. Possible SUDEP cases, an understudied group, were significantly older, and had higher rates of cardiac, drug, or toxicology findings than more certain SUDEP cases. With a potentially contributing or competing COD, ME/Cs favored non-epilepsy-related diagnoses, suggesting a bias toward listing CODs with structural or toxicological findings; SUDEP has no pathognomonic features. A history of epilepsy should always be listed on death certificates and autopsy reports. Even without an alternate COD, ME/Cs infrequently classified COD as "SUDEP." Improved collaboration and communication between epilepsy and ME/C communities improve diagnostic accuracy, as well as bereavement and research opportunities.

Objective In epilepsy, patients who receive appropriate care receive treatment that differs substantially from those that do not. Given the need for a more detailed assessment of the role of specialty referral in the care of patients with epilepsy, this systematic literature review identified epilepsy care guidelines and recommendations that specifically address when and why people with epilepsy should be referred to specialty care. Methods This study identified recent (in the last 10 years) publications that made best-practice recommendations for referring people with epilepsy to a neurologist or epileptologist. We searched six databases in December 2018: MEDLINE (PubMed), Cochrane Library, ProQuest, Web of Science, CINAHL (Ebsco), Scopus (Elsevier). Search terms included “Epilepsy” OR “Seizures,” “Guideline” OR “Practice Parameter,” and “Referral.” Results The 15 full-text articles identified included formal guidelines, summaries of these guidelines, or professional commentary that builds upon existing guidelines. Most of these publications came from the U.K and its National Institute for Health and Care Excellence. Overall, the included recommendations for referral varied considerably both for new-onset and refractory epilepsy. Although these recommendations were not consistent, it is reasonable to refer patients following the failure of 2 anti-seizure medication (ASM) trials. Significance Guidelines and informal recommendations are not consistent regarding best practices for specialty care referral for patients with epilepsy. These guidelines and recommendations should consider the context of care in real-world settings and suggest pragmatic approaches that optimize seizure control and functioning.

BACKGROUND: We investigate the case-ascertainment accuracy for potentially active epilepsy of four administrative healthcare datasets used to identify deceased adults in Scotland.METHODS: In this diagnostic accuracy study, unique patient identifiers were used to link administrative healthcare data for adults (aged 16 years and over) who died in Scotland between 01/01/09-01/01/16. Cases were ascertained from linking mortality records, hospital admissions, antiepileptic drug (AED) prescriptions, and primary care attendances. We assessed ICD-10 codes G40 (epilepsy), G41 (status epilepticus), and R56.8 (seizures) listed as causes of death and as hospital admission reasons, various AEDs, and F25 primary care epilepsy Read codes. These epilepsy indicators were searched through 01/01/09-01/01/16, suggesting active epilepsy during a maximal period of seven years before death. They were compared to epilepsy diagnoses made from medical records reviewed by a senior epileptologist, with a second senior epileptologist independently reviewing the medical records in a 10 % sample to check for specialist interrater agreement in epilepsy diagnoses. We validated how accurately epilepsy was identified by each dataset alone and when combined, calculating positive predictive value (PPV) and sensitivity (with 95 % confidence intervals (CIs)).RESULTS: 159,032 deceased potential epilepsy cases were captured across the four datasets. Medical records reviewed in a random sample of 936 confirmed that epilepsy was present in 614 and absent in 322. Specialist interrater diagnostic agreement was substantial (100 medical records reviewed in duplicate, kappa = 0.72, CI 0.58-0.86). G40-41 cause of death codes had a PPV of 86 % (CI 84-89 %) and sensitivity of 73 % (CI 69-76 %). Adding R56.8 lowered PPV to 69 % (CI 65-72 %) and raised sensitivity to 87 % (CI 84-90 %). The optimal algorithm combining two datasets consisted of F25 Read codes paired with AEDs (PPV 86 % (CI 80-92 %), sensitivity 93 % (CI 88-97 %)). Also effective was pairing G40-41 and/or R56.8 cause of death codes with AEDs (PPV 91 % (CI 89-94 %), sensitivity 81 % (CI 77-84 %)). Whilst algorithms combining three datasets raised PPV to as high as 93-95 %, the associated sensitivities were low (71 % at most).CONCLUSIONS: Routinely-collected Scottish data can accurately identify epilepsy in deceased adults. It may be necessary to combine the diagnostic coding used with AEDs to ensure optimal case-ascertainment. The results help inform the design of future Scottish epilepsy mortality studies recruiting from administrative data sources.

Highlights • Case study of 16 MRI negative epilepsy patients with lesions on 7T. • Patients with definite findings on 7T are more likely to respond to surgery. • Patients with non-definite findings on 7T benefited from surgery at lower rates. • 7T may have further utility in surgical treatment of epilepsy., The objective is to quantitatively assess surgical outcomes in epilepsy patients who underwent scanning at 7T MRI whose lesions were undetectable at conventional field strengths (1.5T/3T). 16 patients who underwent an initial 1.5T/3T scan that was marked as non-lesional by a neuroradiologist and were candidates for epilepsy surgery were scanned at 7T. The 7T findings were evaluated by an expert neuroradiologist blinded to the suspected seizure onset zone (sSOZ). The relation of the neuroradiologist’s findings compared with the sSOZ was classified as non-definite (no 7T lesion or lesion of no epileptogenic significance, or lesion of epileptogenic potential which localizes to the patient’s sSOZ but is not the definitive cause), or definite (7T lesion of epileptogenic potential that highly localizes to the sSOZ and is confirmed through surgical intervention).. Each patient underwent neurosurgical intervention and postoperative Engel outcomes were obtained through retrospective chart review by an epileptologist. Of the 16 patients, 7 had imaging findings of definite epileptogenic potential at 7T while 9 had non-definite imaging findings. 15 out of 16 patients had Engel I, II, or III outcomes indicating worthwhile improvement. Patients with definite lesion status achieved Engel I surgical outcomes at higher rates (57.1%) than patients with non-definite lesion status (33.3%). Patients with normal clinical diagnostic scans at lower field strengths who have definite radiological findings on 7T corresponding to the sSOZ may experience worthwhile improvement from surgical intervention.

Objective In long-term video-monitoring, automatic seizure detection holds great promise as a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) designed to process images of EEG plots demonstrated high performance for seizure detection, but still has room for reducing the false-positive alarm rate. Methods We combined a CNN that processed images of EEG plots with patient-specific autoencoders (AE) of EEG signals to reduce the false alarms during seizure detection. The AE automatically logged abnormalities, i.e., both seizures and artifacts. Based on seizure logs compiled by expert epileptologists and errors made by AE, we constructed a CNN with 3 output classes: seizure, non-seizure-but-abnormal, and non-seizure. The accumulative measure of number of consecutive seizure labels was used to issue a seizure alarm. Results The second-by-second classification performance of AE-CNN was comparable to that of the original CNN. False-positive seizure labels in AE-CNN were more likely interleaved with “non-seizure-but-abnormal” labels than with true-positive seizure labels. Consequently, “non-seizure-but-abnormal” labels interrupted runs of false-positive seizure labels before triggering an alarm. The median false alarm rate with the AE-CNN was reduced to 0.034 h−1, which was one-fifth of that of the original CNN (0.17 h−1). Conclusions A label of “non-seizure-but-abnormal” offers practical benefits for seizure detection. The modification of a CNN with an AE is worth considering because AEs can automatically assign “non-seizure-but-abnormal” labels in an unsupervised manner with no additional demands on the time of the epileptologist.

Highlights • Epileptologists can accurately review single-channel EEG data. • All seizures were visible in single-channel EEG from at least one Epilog location. • Epileptologists review of Epilog EEG is more accurate than personal seizure diaries., Objective Recording seizures using personal seizure diaries can be challenging during everyday life and many seizures are missed or mis-reported. People living with epilepsy could benefit by having a more accurate and objective wearable EEG system for counting seizures that can be used outside of the hospital. The objective of this study was to (1) determine which seizure types can be electrographically recorded from the scalp below the hairline, (2) determine epileptologists’ ability to identify electrographic seizures from single-channels extracted from full-montage wired-EEG, and (3) determine epileptologists’ ability to identify electrographic seizures from Epilog, a wireless single-channel EEG sensor. Methods Epilog sensors were worn concurrently during epilepsy monitoring unit (EMU) monitoring. During standard-of-care review, epileptologists were asked if the electrographic portion of the seizure was visible on single channels of wired electrodes at locations proximal to Epilog sensors, and if focal-onset, which electrode was closest to the focus. From these locations, single channels of EEG extracted from wired full-montage EEG and the proximal Epilog sensor were presented to 3 blinded epileptologists along with markers for when known seizures occurred (taken from the standard-of-care review). Control segments at inter-ictal times were included as control. The epileptologists were asked whether a seizure event was visible in the single channel EEG record at or near the marker. Results A total of 75 seizures were recorded from 22 of 40 adults that wore Epilog during their visit to the EMU. Epileptologists were able to visualize known seizure activity on at least one of the wired electrodes proximal to Epilog sensors for all seizure events. Epileptologists accurately identified seizures in 71% of Epilog recordings and 84% of single-channel wired recordings and were 92% accurate identifying seizures with Epilog when those seizures ended in a clinical convulsion compared to those that did not (>55%). Conclusions Epileptologists are able to visualize seizure activity on single-channels of EEG at locations where Epilog sensors are easily placed on the scalp below hairline. Manual review of seizure annotations can be done quickly and accurately (>70% TP and >98% PPV) on single-channel EEG data. Reviewing single-channel EEG is more accurate than what has been reported in the literature on self-reporting seizures in seizure diaries, the current standard of care for seizure counting outside of the EMU. Significance Wearable EEG will be important for seizure monitoring outside of the hospital. Epileptologists can accurately identify seizures in single-channel EEG, better than patient self-reporting in diaries based on the literature. Automated or semi-automated seizure detection on single channels of EEG could be used in the future to objectively count seizures to complement the standard of care outside of the EMU without the overt burden upon epileptologist review.

Pontocerebellar hypoplasia (PCH) is an autosomal recessive neurodevelopmental and neurodegenerative disorder characterized by cerebellar and pontine hypoplasia, progressive microcephaly, and developmental delay. Ten types of PCH have been described; PCH type 2A (PCH2A) due to a mutation in TSEN54 is the most frequent. Seizures have been reported in the large majority of patients. The probability of epilepsy developing increases with age, along with difficulties in differentiating seizures from dyskinetic movements. The aim of the present report was to describe the clinical symptoms and electroencephalogram (EEG) changes over time in three patients of Israeli Arab origin with PCH2A. All three, including two siblings and their first cousin, were homozygous for the TSEN54 p.A304S mutation. The patients demonstrated profound psychomotor retardation, severe spasticity and contractures, choreoathetoid movements, and seizures. The magnetic resonance imaging (MRI) scans and EEGs were reviewed by an experienced neuroradiologist and epileptologist, respectively. The MRI scans revealed a dragonfly-like cerebellar pattern in all patients. Despite the normal early EEG findings, all patients had characteristic features of epilepsy, with tonic seizures starting in the first days to months followed by focal to bilateral tonic–clonic seizures in early childhood which continued to adolescence. In conclusion, patients with PCH2A due to the missense mutation p.A304S in TSEN54 exhibit profound psychomotor delay, movement disorders, and intractable epilepsy. An evolution of EEG abnormalities and seizure semiology occurs over time. Similar to several other genetic epileptic encephalopathies, the normal early EEG tracing does not rule out the later occurrence of epilepsy

Classification of seizure type is a key step in the clinical process for evaluating an individual who presents with seizures. It determines the course of clinical diagnosis and treatment, and its impact stretches beyond the clinical domain to epilepsy research and the development of novel therapies. Automated identification of seizure type may facilitate understanding of the disease, and seizure detection and prediction has been the focus of recent research that has sought to exploit the benefits of machine learning and deep learning architectures. Nevertheless, there is not yet a definitive solution for automating the classification of seizure type, a task that must currently be performed by an expert epileptologist. Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data. We first explore the performance of traditional deep learning techniques which use convolutional and recurrent neural networks, and enhance these architectures by using external memory modules with trainable neural plasticity. We show that our model achieves a state-of-the-art weighted F1 score of 0.945 for seizure type classification on the TUH EEG Seizure Corpus with the IBM TUSZ preprocessed data. This work highlights the potential of neural memory networks to support the field of epilepsy research, along with biomedical research and signal analysis more broadly., Proceedings of the IEEE International Conference of Engineering in Medicine and Biology Society. 2020

Presurgical evaluation and surgery in the pediatric age group are unique in challenges related to caring for the very young, range of etiologies, choice of appropriate investigations, and surgical procedures. Accepted standards that define the criteria for levels of presurgical evaluation and epilepsy surgery care do not exist. Through a modified Delphi process involving 61 centers with experience in pediatric epilepsy surgery across 20 countries, including low-middle- to high-income countries, we established consensus for two levels of care. Levels were based on age, etiology, complexity of presurgical evaluation, and surgical procedure. Competencies were assigned to the levels of care relating to personnel, technology, and facilities. Criteria were established when consensus was reached (≥75% agreement). Level 1 care consists of children age 9 years and older, with discrete lesions including hippocampal sclerosis, undergoing lobectomy or lesionectomy, preferably on the cerebral convexity and not close to eloquent cortex, by a team including a pediatric epileptologist, pediatric neurosurgeon, and pediatric neuroradiologist with access to video-electroencephalography and 1.5-T magnetic resonance imaging (MRI). Level 2 care, also encompassing Level 1 care, occurs across the age span and range of etiologies (including tuberous sclerosis complex, Sturge-Weber syndrome, hypothalamic hamartoma) associated with MRI lesions that may be ill-defined, multilobar, hemispheric, or multifocal, and includes children with normal MRI or foci in/abutting eloquent cortex. Available Level 2 technologies includes 3-T MRI, other advanced magnetic resonance technology including functional MRI and diffusion tensor imaging (tractography), positron emission tomography and/or single photon emission computed tomography, source localization with electroencephalography or magnetoencephalography, and the ability to perform intra- or extraoperative invasive monitoring and functional mapping, by a large multidisciplinary team with pediatric expertise in epilepsy, neurophysiology, neuroradiology, epilepsy neurosurgery, neuropsychology, anesthesia, neurocritical care, psychiatry, and nursing. Levels of care will improve safety and outcomes for pediatric epilepsy surgery and provide standards for personnel and technology to achieve these levels.

To identify the differences in the level of subjective well-being in adult people in the process of refining and establishing the diagnosis of epilepsy.The study enrolled in total 67 people, including 37 patients, aged from 24 to 51 years, referred to the epileptologist to refine the diagnosis of epilepsy, 15 age-matched healthy controls and 15 age-matched controls with neurological diseases. The Subjective Well-Being Scale modified by M.V. Sokolova was used.An individual experiences negative emotional feelings during the process of refining and establishing the diagnosis of epilepsy, which are strengthened by the specifics of dysphoric disorders in epilepsy. This decreases the level of self-reported subjective well-being that is convincingly confirmed by psychodiagnostic methods.Цель исследования. Определение наличия различий в уровне субъективного благополучия у взрослых людей, проходящих процедуру уточнения и постановки диагноза эпилепсии. Материал и методы. Общее число выборки составило 67 человек. В выборку были включены 37 пациентов в возрасте от 24 до 51 года, направленных на консультацию к эпилептологу для уточнения диагноза эпилепсии. Контрольные группы составили 15 человек аналогичного возраста без диагноза эпилепсия, не имеющих неврологических заболеваний, и 15 человек аналогичного возраста без диагноза эпилепсии, имеющих неврологические заболевания. Для выявления уровня субъективного благополучия применяли диагностическую методику 'Шкала субъективного благополучия' в адаптации М.В. Соколовой. Результаты и заключение. В условиях уточнения и постановки диагноза эпилепсии личность испытывает отрицательные эмоциональные переживания, отягощенные и самой спецификой дисфорических расстройств при эпилепсии. Это закономерно снижает восприятие больным собственного субъективного благополучия, что достаточно убедительно выявляется с помощью психодиагностики.

Objective Postictal generalized electroencephalographic suppression (PGES) is a pattern of low-voltage scalp electroencephalographic (EEG) activity following termination of generalized seizures. PGES has been associated with both sudden unexplained death in patients with epilepsy and therapeutic efficacy of electroconvulsive therapy (ECT). Automated detection of PGES epochs may aid in reliable quantification of this phenomenon. Methods We developed a voltage-based algorithm for detecting PGES. This algorithm applies existing criteria to simulate expert epileptologist readings. Validation relied on postictal EEG recording from patients undergoing ECT (NCT02761330), assessing concordance among the algorithm and four clinical epileptologists. Results We observed low-to-moderate concordance among epileptologist ratings of PGES. Despite this, the algorithm displayed high discriminability in comparison to individual epileptologists (c-statistic range: 0.86-0.92). The algorithm displayed high discrimination (c-statistic: 0.91) and substantial peak agreement (Cohen’s Kappa: 0.65) in comparison to a consensus of clinical ratings. Interrater agreement between the algorithm and individual epileptologists was on par with that among expert epileptologists. Conclusions An automated voltage-based algorithm can be used to detect PGES following ECT, with discriminability nearing that of experts. Significance Algorithmic detection may support clinical readings of PGES and improve precision when correlating this marker with clinical outcomes following generalized seizures.

Purpose The purpose of this study was to investigate the sensitivity and specificity of a standardized placebo protocol using a moist swab pad application in children and adolescents with psychogenic seizures vs epileptic seizures. Methods We retrospectively reviewed clinical data and video-EEG monitoring records with the standardized placebo protocol of 408 patients. Video -EEG diagnosis with PNES-consistent semiology was made in the context of clinical data by a two-certified epileptologist. Results of induction of psychogenic seizure by moist swab pad application were analyzed in 158 patients with PNES. A control group was composed of 74 patients with epilepsy in which induction was performed. Results Sensitivity of placebo test for the diagnosis of PNES was 81.1%, specificity 79.8%, positive predictive value 89.6% and negative predictive value 66.3%. Conclusion The placebo technique with a moist swab can be regarded as helpful in triggering a psychogenic episode in children and adolescents.

Background: The DX-Seizure study aims to evaluate the diagnostic accuracy (sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratio) of the ambulatory EEG in comparison with the first routine EEG, and a second routine EEG right before the ambulatory EEG, on adult patients with first single unprovoked seizure (FSUS) and define the utility of ambulatory EEG in forecasting seizure recurrence in these patients after 1-year follow-up. Methods: The DX-Seizure study is a prospective cohort of 113 adult patients (≥18-year-old) presenting with FSUS to the Single Seizure Clinic for evaluation. These patients will be assessed by a neurologist/epileptologist with the first routine EEG (referral EEG) and undergo a second routine EEG and ambulatory EEG. The three EEG (first routine EEG as gold standard) will be compared and evaluated their diagnostic accuracy (sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios) with respect of epileptiform activity and other abnormalities. One-year follow-up of each patient will be used to assess recurrence of seizures after a FSUS and the utility of the ambulatory EEG to forecast these recurrences. Discussion: To the best of our knowledge, this will be the first study to prospectively examine the use of ambulatory EEG for a FSUS in adults and its use for prediction of recurrence of seizures. The overarching goal is to improve diagnostic accuracy with the use of ambulatory EEG in patients with their FSUS. We anticipate that this will decrease incorrect or uncertain diagnoses with resulting psychological and financial cost to the patient. We also anticipate that an improved method to predicting the recurrence of seizures will reduce the chances of repeated seizures and their consequences.

This chapter looks at the experience of an Epileptologist. The Epileptologist insists that he or she is not tired of seeing patients with epileptic seizures or Psychogenic Non-Epileptic Seizures (PNES), nor are they tired of reading EEGs, returning patient phone calls, and signing prescriptions. However, the Epileptologist is tired of misinformation and of inappropriate medication trials. The Epileptologist is also tired of working with reluctant Psychiatrists and dealing with Neurologists who insist on treating “organic” disease. As such, after having thought long and hard about how to provide a clear and concise message to patients with PNES or Non-Epileptic Seizures, the Epileptologist decided to frame the response as an FAQ (frequently asked questions). The chapter then identifies the important questions that patients should ask their neurologists.