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Persons with drug-resistant epilepsy may benefit from epilepsy surgery and should undergo presurgical testing to determine potential candidacy and appropriate intervention. Institutional expertise can influence use and availability of evaluations and epilepsy surgery candidacy. This census survey study aims to examine the influence of geographic region and other center characteristics on presurgical testing for medically intractable epilepsy.We analyzed annual report and supplemental survey data reported in 2020 from 206 adult epilepsy center directors and 136 pediatric epilepsy center directors in the United States. Test utilization data were compiled with annual center volumes, available resources, and US Census regional data. We used Wilcoxon rank-sum, Kruskal-Wallis, and chi-squared tests for univariate analysis of procedure utilization. Multivariable modeling was also performed to assign odds ratios (ORs) of significant variables.The response rate was 100% with individual element missingness 11% across 342 observations undergoing univariate analysis. A total of 278 complete observations were included in the multivariable models, and significant regional differences were present. For instance, compared to centers in the South, those in the Midwest used neuropsychological testing (OR = 2.87, 95% confidence interval [CI] = 1.2-6.86; p = .018) and fluorodeoxyglucose-positron emission tomography (OR = 2.74, 95% CI = = 1.14-6.61; p = .025) more commonly. For centers in the Northeast (OR = .46, 95% CI = .23-.93; p = .031) and West (OR = .41, 95% CI = .19-.87; p = .022), odds of performing single-photon emission computerized tomography were lower by nearly 50% compared to those in the South. Center accreditation level, demographics, volume, and resources were also associated with varying individual testing rates.Presurgical testing for drug-resistant epilepsy is influenced by US geographic region and other center characteristics. These findings have potential implications for comparing outcomes between US epilepsy centers and may inject disparities in access to surgical treatment.

Background and ObjectiveNearly one-third of persons with epilepsy will continue having seizures despite trialing multiple antiseizure medications. Epilepsy surgery may be beneficial in these cases, and evaluation at a comprehensive epilepsy center is recommended. Numerous palliative and potentially curative approaches exist, and types of surgery performed may be influenced by center characteristics. This article describes epilepsy center characteristics associated with epilepsy surgery access and volumes in the United States.MethodsWe analyzed National Association of Epilepsy Centers 2019 annual report and supplemental survey data obtained with responses from 206 adult epilepsy center directors and 136 pediatric epilepsy center directors in the United States. Surgical treatment volumes were compiled with center characteristics, including US Census region. We used multivariable modeling with zero-inflated Poisson regression models to present ORs and incidence rate ratios of receiving a given surgery type based on center characteristics.ResultsThe response rate was 100% with individual element missingness less than 4% across 352 observations undergoing univariate analysis. Multivariable models included 319 complete observations. Significant regional differences were present. The rates of laser interstitial thermal therapy (LITT) were lower at centers in the Midwest (incidence rate ratio [IRR] 0.74, 95% CI 0.59–0.92;p= 0.006) and Northeast (IRR 0.77, 95% CI 0.61–0.96;p= 0.022) compared with those in the South. Conversely, responsive neurostimulation implantation rates were higher in the Midwest (IRR 1.45, 95% CI 1.1–1.91;p= 0.008) and West (IRR 1.91, 95% CI 1.49–2.44;p< 0.001) compared with the South. Center accreditation level, institution type, demographics, and resources were also associated with variations in access and rates of potentially curative and palliative surgical interventions.DiscussionEpilepsy surgery procedure volumes are influenced by US epilepsy center region and other characteristics. These variations may affect access to specific surgical treatments for persons with drug resistant epilepsy across the United States.

BACKGROUND AND OBJECTIVES: Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as “ictal-interictal-injury continuum” (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS: We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS: SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and “other” classes, SPaRCNet exceeds the following percentages of 20 experts—ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION: SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Background and ObjectivesPersons with epilepsy, especially those with drug resistant epilepsy (DRE), may benefit from inpatient services such as admission to the epilepsy monitoring unit (EMU) and epilepsy surgery. The COVID-19 pandemic caused reductions in these services within the US during 2020. This article highlights changes in resources, admissions, and procedures among epilepsy centers accredited by the National Association of Epilepsy Centers (NAEC).MethodsWe compared data reported in 2019, prior to the COVID-19 pandemic, and 2020 from all 260 level 3 and level 4 NAEC accredited epilepsy centers. Data were described using frequency for categorical variables and median for continuous variables and were analyzed by center level, center population category, and geographical location. Qualitative responses from center directors to questions regarding the impact from COVID-19 were summarized utilizing thematic analysis. Responses from the NAEC center annual reports as well as a supplemental COVID-19 survey were included.ResultsEMU admissions declined 23% (-21,515) in 2020, with largest median reductions in level 3 centers [-55 admissions (-44%)] and adult centers [-57 admissions (-39%)]. The drop in admissions was more substantial in the East North Central, East South Central, Mid Atlantic, and New England US Census divisions. Survey respondents attributed reduced admissions to re-assigning EMU beds, restrictions on elective admissions, reduced staffing, and patient reluctance for elective admission. Treatment surgeries declined by 371 cases (5.7%), with the largest reduction occurring in VNS implantations [-486 cases (-19%)] and temporal lobectomies [-227 cases (-16%)]. All other procedure volumes increased, including a 35% (54 cases) increase in corpus callosotomies.DiscussionIn the US, access to care for persons with epilepsy declined during the COVID-19 pandemic in 2020. Adult patients, those relying on level 3 centers for care, and many persons in the eastern half of the US were most affected.

Objective Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood. Methods We developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error. Results Model performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14]. Conclusions These results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

BACKGROUND AND OBJECTIVES: The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as “ictal-interictal-injury continuum” (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS: This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as “seizure (SZ),” “lateralized periodic discharges (LPDs),” “generalized periodic discharges (GPDs),” “lateralized rhythmic delta activity (LRDA),” “generalized rhythmic delta activity (GRDA),” or “other.” EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS: Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Image: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Image: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION: Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

Discrimination in the workplace when documented is illegal but is seen to still exist in some forms whether based on culture, race, or gender. Each of these disparities warrants further discussion and study because of their significant impacts on hiring decisions, career advancement, and compensation. In this article, the authors have focused their attention on gender disparity in the fields of neurology and clinical neurophysiology and shared the data currently available to them. At a time when the field of clinical neurophysiology has seen enormous growth, gender disparity in leadership and compensation remain. Despite the increasing number of women entering the fields of neurology and clinical neurophysiology, women remain underrepresented in national leadership positions. Many women physicians report experiencing gender discrimination despite increasing efforts by universities and medical centers to improve inclusivity and diversity. Equity and inclusivity are not the same and there is a disconnect between the increased numbers of women and their shared experiences in the workplace. Implicit bias undermines the ability of women to advance in their careers. For neurologists, data indicate that the latest gender pay gap is $56,000 (24%), increased from $37,000 in 2015, and is one of the largest pay gaps in any medical specialty. One third of the top 12 medical schools in the United States require that maternity leave be taken through disability coverage and/or sick benefits, and most family leave policies constrain benefits to the discretion of departmental leadership. The authors recommend strategies to improve gender disparity include institutional training to Identify and overcome biases, changes to professional organizations and national scientific meeting structure, transparency in academic hiring, promotion and compensation, and mentorship and sponsorship programs.

ObjectiveTo incorporate standardized documentation into an epilepsy clinic and to use these standardized data to compare patients' perception of epilepsy diagnosis to provider documentation.MethodsUsing quality improvement methodology, we implemented interventions to increase documentation of epilepsy diagnosis, seizure frequency, and type from 49.8% to 70% of adult nonemployee patients seen by 6 providers over 5 months of routine clinical care. The main intervention consisted of an interactive SmartPhrase that mirrored a documentation template developed by the Epilepsy Learning Healthcare System. We assessed the weekly proportion of complete SmartPhrases among eligible patient encounters with a statistical process control chart. We used a subset of patients with established epilepsy care linked to existing patient-reported survey data to examine the proportion of patient-to-provider agreement on epilepsy diagnosis (yes vs no/unsure). We also examined sociodemographic and clinical characteristics of patients who disagreed vs agreed with provider's documentation of epilepsy diagnosis.ResultsThe median SmartPhrase weekly completion rate was 78%. Established patients disagreed with providers with respect to epilepsy diagnosis in 18.5% of encounters (κ = 0.13), indicating that they did not have or were unsure if they had epilepsy despite having a provider-documented epilepsy diagnosis. Patients who disagreed with providers were similar to those who agreed with respect to age, sex, ethnicity, marital status, seizure frequency, type, and other quality-of-life measures.ConclusionThis project supports the feasibility of implementing standardized documentation of data relevant to epilepsy care in a tertiary epilepsy clinic and highlights an opportunity for improvement in patient-provider communication.

PURPOSE High-density EEG (HD-EEG) systems and electrical source imaging techniques have revolutionized our ability to assess the potential sources of epileptiform activity and other EEG features. Nonetheless, clinical use of HD-EEG is hampered by the lack of a standardized electrode nomenclature system and the inherent difficulties encountered in visually reviewing recordings. Inefficient visual review of HD-EEG remains a major barrier to incorporating these techniques into routine clinical care. METHODS Extension of the 10-10 is first defined by the addition of 2 reference curves: the -10% and -20% axial reference curves. Electrode positions over the face are named based on facial bony structures (N = nasion, Z = zygomatic prominence, M = mandible) and over the back of the head on posterior landmarks (I = inion, S = subinion, B = Base). Then, following the 10% incremental distance rule, we define additional electrode positions. Electrodes with nonstandard positions are clustered around the closest 10-10 electrode, deemed their cardinal point. RESULTS The 256-electrode Geodesic Sensor Net mapped to 96 of the 120 extended 10-10 cardinal electrodes. CONCLUSIONS Electrode position nomenclature that builds upon the international standard 10-10 system allows electroencephalographers to identify spatial areas of interest in HD-EEG relative to positions in routine use. A standard viewing montage for HD-EEG and its application with electrical source imaging boost efficiency when reviewing data and improve accuracy in recognizing epileptiform discharges. Additionally, our proposed system is not limited to a specific HD-EEG system, electrode count, or electrode layout.

Seizure documentation is an essential component of epilepsy management. Not all persons with epilepsy choose to document their seizures, but many view the practice as essential to managing their disease. While seizure documentation is a valuable aspect of patient care, clinicians and patients must remain aware that seizure underreport and overreport commonly occur due to lack of seizure awareness. Additionally, in rare cases, persons with epilepsy may intentionally conceal their seizures from clinicians. The continued development of electronic seizure diaries and epilepsy self-management software provides patients with new and expanding options for seizure documentation and disease management. In order for these tools to be utilized most effectively, patient input must be central to their development. Given the limitations of seizure documentation, the development of accurate, non-invasive seizure detection devices is crucial for accurate seizure monitoring.

Deep brain stimulation of the anterior nuclei of thalamus (ANT-DBS) is effective for reduction of seizures, but little evidence is available to guide practitioners in the practical use of this therapy. In an attempt to fill this gap, a questionnaire with 37 questions was circulated to 578 clinicians who were either engaged in clinical trials of or known users of DBS for epilepsy, with responses from 141, of whom 58.2% were epileptologists and 28.4% neurosurgeons. Multiple regions of the world were represented. The survey found that the best candidates for DBS were considered those with temporal or frontal seizures, refractory to at least two medicines. Motivations for renewing therapy upon battery depletion were reduced convulsive, impaired awareness, and severe seizures and improved quality of life. Targeting of leads mainly was by magnetic resonance imaging, sometimes with intraoperative imaging or microelectrode recording. The majority used transventricular approaches. Stimulation parameters mostly imitated the SANTE study parameters, except for initial stimulation amplitudes in the 2-3-V or -mA range, versus 5 V in the SANTE study. Stimulation intensity was most often increased or reduced, respectively, for lack of efficacy or side effects, but changes in active contacts, cycle time, and pulse duration were also employed. Mood or memory problems or paresthesias were the side effects most responsible for adjustments. Off-label sites stimulated included centromedian thalamus, hippocampus, neocortex, and a few others. Several physicians used DBS in conjunction with vagus nerve stimulation or responsive neurostimulation, although our study did not track efficacy for combined use. Experienced users varied more from published parameters than did inexperienced users. In conclusion, surveys of experts can provide Class IV evidence for the most prevalent practical use of ANT-DBS. We present a flowchart for one protocol combining common practices. Controlled comparisons will be needed to choose the best approach.

SUMMARY High-density EEG (HD-EEG) recordings use a higher spatial sampling of scalp electrodes than a standard 10-20 low-density EEG montage. Although several studies have demonstrated improved localization of the epileptogenic cortex using HD-EEG, widespread implementation is impeded by cost, setup and interpretation time, and lack of specific or sufficient procedural billing codes. Despite these barriers, HD-EEG has been in use at several institutions for years. These centers have noted utility in a variety of clinical scenarios where increased spatial resolution from HD-EEG has been required, justifying the extra time and cost. We share select scenarios from several centers, using different recording techniques and software, where HD-EEG provided information above and beyond the standard low-density EEG. We include seven cases where HD-EEG contributed directly to current clinical care of epilepsy patients and highlight two novel techniques which suggest potential opportunities to improve future clinical care. Cases illustrate how HD-EEG allows clinicians to: case 1-lateralize falsely generalized interictal epileptiform discharges; case 2-improve localization of falsely generalized epileptic spasms; cases 3 and 4-improve localization of interictal epileptiform discharges in anatomic regions below the circumferential limit of standard low-density EEG coverage; case 5-improve noninvasive localization of the seizure onset zone in lesional epilepsy; cases 6 and 7-improve localization of the seizure onset zone to guide invasive investigation near eloquent cortex; case 8-identify epileptic fast oscillations; and case 9-map language cortex. Together, these nine cases illustrate that using both visual analysis and advanced techniques, HD-EEG can play an important role in clinical management.

Objectives: Seizures and seizure-like electroencephalography (EEG) patterns, collectively referred to as “ictal interictal injury continuum” (IIIC) patterns, are commonly encountered in critically ill patients. Automated detection is important for patient care and to enable research. However, training accurate detectors requires a large labeled dataset. Active Learning (AL) may help select informative examples to label, but the optimal AL approach remains unclear. Methods: We assembled >200,000 h of EEG from 1,454 hospitalized patients. From these, we collected 9,808 labeled and 120,000 unlabeled 10-second EEG segments. Labels included 6 IIIC patterns. In each AL iteration, a Dense-Net Convolutional Neural Network (CNN) learned vector representations for EEG segments using available labels, which were used to create a 2D embedding map. Nearest-neighbor label spreading within the embedding map was used to create additional pseudo-labeled data. A second Dense-Net was trained using real- and pseudo-labels. We evaluated several strategies for selecting candidate points for experts to label next. Finally, we compared two methods for class balancing within queries: standard balanced-based querying (SBBQ), and high confidence spread-based balanced querying (HCSBBQ). Results: Our results show: 1) Label spreading increased convergence speed for AL. 2) All query criteria produced similar results to random sampling. 3) HCSBBQ query balancing performed best. Using label spreading and HCSBBQ query balancing, we were able to train models approaching expert-level performance across all pattern categories after obtaining ∼7000 expert labels. Conclusion: Our results provide guidance regarding the use of AL to efficiently label large EEG datasets in critically ill patients., SCOPUS: ar.j, info:eu-repo/semantics/published

SCOPUS: ar.j, info:eu-repo/semantics/published

OBJECTIVES: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions. DESIGN: Retrospective. SETTING: ICUs at four academic medical centers in the United States. PATIENTS: Comatose patients with acute hypoxic-ischemic encephalopathy.None. MEASUREMENTS AND MAIN RESULTS: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated. CONCLUSIONS: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance., SCOPUS: ar.j, info:eu-repo/semantics/published

Objective The optimal treatment of nonconvulsive seizures in critically ill patients is uncertain. We evaluated the comparative effectiveness of the antiseizure drugs lacosamide (LCM) and fosphenytoin (fPHT) in this population. Methods The TRENdS (Treatment of Recurrent Electrographic Nonconvulsive Seizures) study was a noninferiority, prospective, multicenter, randomized treatment trial of patients diagnosed with nonconvulsive seizures (NCSs) by continuous electroencephalography (cEEG). Treatment was randomized to intravenous (IV) LCM 400mg or IV fPHT 20mg phenytoin equivalents/kg. The primary endpoint was absence of electrographic seizures for 24 hours as determined by 1 blinded EEG reviewer. The frequency with which NCS control was achieved in each arm was compared, and the 90% confidence interval (CI) was determined. Noninferiority of LCM to fPHT was to be concluded if the lower bound of the CI for relative risk was >0.8. Results Seventy-four subjects were enrolled (37 LCM, 37 fPHT) between August 21, 2012 and December 20, 2013. The mean age was 63.6 years; 38 were women. Seizures were controlled in 19 of 30 (63.3%) subjects in the LCM arm and 16 of 32 (50%) subjects in the fPHT arm. LCM was noninferior to fPHT (p = 0.02), with a risk ratio of 1.27 (90% CI = 0.88-1.83). Treatment emergent adverse events (TEAEs) were similar in both arms, occurring in 9 of 35 (25.7%) LCM and 9 of 37 (24.3%) fPHT subjects (p = 1.0). Interpretation LCM was noninferior to fPHT in controlling NCS, and TEAEs were comparable. LCM can be considered an alternative to fPHT in the treatment of NCSs detected on cEEG. Ann Neurol 2018;83:1174-1185.

Objective The objective of the study was to identify functioning and quality-of-life (QOL) patient-reported outcome measurements (PROMs) feasible for use in the waiting room of adult epilepsy clinics. Material and methods We searched PubMed and Web of Science for articles on in English, Spanish, Portuguese, Italian, and French published by the end of February 15th, 2019. We screened retrieved titles and abstracts looking for publications that reported the use of PROMs to measure functioning and QOL in epilepsy. The authors, clinical experts, and patient advocates from the Epilepsy Foundation of America conceptualized a set of desirable feasibility attributes for PROMs implementation in the waiting room of adult epilepsy clinics. These attributes included brief time for completion (i.e., ≤ 3 min), free cost, coverage of four minimum QOL domains and respective facets, and good evidence of psychometric properties. We defined QOL domains according to the World Health Organization's classification and created psychometric appraisal criteria based on the Food and Drug Administration's (FDA) Guidance. Results Eighteen candidate instruments were identified and compared with respect to desirable attributes for use in adult epilepsy clinics. We found that the Quality-of-life in epilepsy (QOLIE)-10 and Patient-Reported Outcome Measurement Information System-10 (PROMIS-10) were the most feasible PROMs for implementation in adult epilepsy clinics based on our criteria. The QOLIE-10 and PROMIS-10 still lack ideal evidence of responsiveness in people with epilepsy. Conclusion This is the first systematic review that aimed to assess feasibility properties of available functioning and QOL PROMs. The QOLIE-10 and PROMIS-10 are potentially feasible instruments for implementation in the waiting room of adult epilepsy clinics. Further studies assessing the responsiveness of these PROMs are needed and will contribute to the selection of the most appropriate instrument for longitudinal use in adult epilepsy clinical practice.

Objective To describe the organization of the Epilepsy Learning Healthcare System (ELHS), a network that aims to improve care outcomes for people with epilepsy (PWE). Materials and Methods Patients and family partners, providers, researchers, epidemiologists, and other leaders collaborated to recruit epilepsy centers and community services organizations into a novel learning network. A multidisciplinary Coordinating Committee developed ELHS governance and organizational structure, including four key planning Cores (Community, Clinical, Quality Improvement, and Data). Through Quality Improvement (QI) methodology grounded in the Institute for Healthcare Improvement (IHI) model, including iterative Plan-Do-Study-Act (PDSA) rapid learning cycles and other learning and sharing sessions, ELHS equipped epilepsy centers and community organizations with tools to standardize, measure, share, and improve key aspects of epilepsy care. The initial learning cycles addressed provider documentation of seizure frequency and type, and also screening for medication adherence barriers. Rapid learning cycles have been carried out on these initial measures in both clinical centers and community-based settings. Additional key measures have been defined for quality of life, screening, and treatment for mental health and behavioral comorbidities, transition from pediatric to adult care, counseling for women and girls living with epilepsy, referral for specialty care, and prevention and treatment of seizure clusters and status epilepticus. Results It is feasible to adopt a learning healthcare system framework in epilepsy centers and community services organizations. Through structured collaboration between epilepsy care providers, community support organizations, PWE, and their families/caregivers we have identified new opportunities to improve outcomes that are not available in traditional care models.

Objective To evaluate the sensitivity of quantitative EEG (QEEG) for electrographic seizure identification in the intensive care unit (ICU). Methods Six-hour EEG epochs chosen from 15 patients underwent transformation into QEEG displays. Each epoch was reviewed in 3 formats: raw EEG, QEEG + raw, and QEEG-only. Epochs were also analyzed by a proprietary seizure detection algorithm. Nine neurophysiologists reviewed raw EEGs to identify seizures to serve as the gold standard. Nine other neurophysiologists with experience in QEEG evaluated the epochs in QEEG formats, with and without concomitant raw EEG. Sensitivity and false-positive rates (FPRs) for seizure identification were calculated and median review time assessed. Results Mean sensitivity for seizure identification ranged from 51% to 67% for QEEG-only and 63%-68% for QEEG + raw. FPRs averaged 1/h for QEEG-only and 0.5/h for QEEG + raw. Mean sensitivity of seizure probability software was 26.2%-26.7%, with FPR of 0.07/h. Epochs with the highest sensitivities contained frequent, intermittent seizures. Lower sensitivities were seen with slow-frequency, low-amplitude seizures and epochs with rhythmic or periodic patterns. Median review times were shorter for QEEG (6 minutes) and QEEG + raw analysis (14.5 minutes) vs raw EEG (19 minutes; p = 0.00003). Conclusions A panel of QEEG trends can be used by experts to shorten EEG review time for seizure identification with reasonable sensitivity and low FPRs. The prevalence of false detections confirms that raw EEG review must be used in conjunction with QEEG. Studies are needed to identify optimal QEEG trend configurations and the utility of QEEG as a screening tool for non-EEG personnel. Classification of evidence review This study provides Class II evidence that QEEG + raw interpreted by experts identifies seizures in patients in the ICU with a sensitivity of 63%-68% and FPR of 0.5 seizures per hour.

The rapid expansion of the use of continuous critical care electroencephalogram (cEEG) monitoring and resulting multicenter research studies through the Critical Care EEG Monitoring Research Consortium has created the need for a collaborative data sharing mechanism and repository. The authors describe the development of a research database incorporating the American Clinical Neurophysiology Society standardized terminology for critical care EEG monitoring. The database includes flexible report generation tools that allow for daily clinical use.Key clinical and research variables were incorporated into a Microsoft Access database. To assess its utility for multicenter research data collection, the authors performed a 21-center feasibility study in which each center entered data from 12 consecutive intensive care unit monitoring patients. To assess its utility as a clinical report generating tool, three large volume centers used it to generate daily clinical critical care EEG reports.A total of 280 subjects were enrolled in the multicenter feasibility study. The duration of recording (median, 25.5 hours) varied significantly between the centers. The incidence of seizure (17.6%), periodic/rhythmic discharges (35.7%), and interictal epileptiform discharges (11.8%) was similar to previous studies. The database was used as a clinical reporting tool by 3 centers that entered a total of 3,144 unique patients covering 6,665 recording days.The Critical Care EEG Monitoring Research Consortium database has been successfully developed and implemented with a dual role as a collaborative research platform and a clinical reporting tool. It is now available for public download to be used as a clinical data repository and report generating tool.

Objective To evaluate the accuracy of ICD-10-CM claims-based definitions for epilepsy and classifying seizure types in the outpatient setting. Methods We reviewed electronic health records (EHR) for a cohort of adults aged 18+ years seen by six neurologists who had an outpatient visit at a level 4 epilepsy center between 01/2019−09/2019. The neurologists used a standardized documentation template to capture the diagnosis of epilepsy (yes/no/unsure), seizure type (focal/generalized/unknown), and seizure frequency in the EHR. Using linked ICD-10-CM codes assigned by the provider, we assessed the accuracy of claims-based definitions for epilepsy, focal seizure type, and generalized seizure type against the reference-standard EHR documentation by estimating sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV). Results There were 673 eligible outpatient encounters. After review of EHRs for standardized documentation, an analytic sample consisted of 520 encounters representing 402 unique patients. In the EHR documentation, 93.5 % (n = 486/520) of encounters were with patients with a diagnosis of epilepsy. Of those, 66.0 % (n = 321/486) had ≥1 focal seizure, 41.6 % (n = 202/486) had ≥1 generalized seizure, and 7% (n = 34/486) had ≥1 unknown seizure. An ICD-10-CM definition for epilepsy (i.e., ICD-10 G40.X) achieved Sn = 84.4 % (95 % CI 80.8−87.5%), Sp = 79.4 % (95 % CI 62.1−91.3%), PPV = 98.3 % (95 % CI 96.6−99.3%), and NPV = 26.2 % (95 % CI 18.0−35.8%). The classification of focal vs generalized/unknown seizures achieved Sn = 69.8 % (95 % CI 64.4−74.8%), Sp = 79.4 % (95 % CI 72.4−85.3%), PPV = 86.8 % (95 % CI 82.1−90.7%), and NPV = 57.5 % (95 % CI 50.8−64.0%). Conclusions Claims-based definitions using groups of ICD-10-CM codes assigned by neurologists in routine outpatient clinic visits at a level 4 epilepsy center performed well in discriminating between patients with and without a diagnosis of epilepsy and between seizure types.

PURPOSE: The aim of this study was to assess the variability in EEG reactivity evaluation practices during cardiac arrest prognostication. METHODS: A survey of institutional representatives from North American academic hospitals participating in the Critical Care EEG Monitoring Research Consortium was conducted to assess practice patterns involving EEG reactivity evaluation. This 10-question multiple-choice survey evaluated metrics related to technical, interpretation, personnel, and procedural aspects of bedside EEG reactivity testing and interpretation specific to cardiac arrest prognostication. One response per hospital was obtained. RESULTS: Responses were received from 25 hospitals, including 7 pediatric hospitals. A standardized EEG reactivity protocol was available in 44% of centers. Sixty percent of respondents believed that reactivity interpretation was subjective. Reactivity bedside testing always (100%) started during hypothermia and was performed daily during monitoring in the majority (71%) of hospitals. Stimulation was performed primarily by neurodiagnostic technologists (76%). The mean number of activation procedures modalities tested was 4.5 (SD 2.1). The most commonly used activation procedures were auditory (83.3%), nail bed pressure (63%), and light tactile stimuli (63%). Changes in EEG amplitude alone were not considered consistent with EEG reactivity in 21% of centers. CONCLUSIONS: There is substantial variability in EEG reactivity evaluation practices during cardiac arrest prognostication among North American academic hospitals. Efforts are needed to standardize protocols and nomenclature according with national guidelines and promote best practices in EEG reactivity evaluation., SCOPUS: ar.j, info:eu-repo/semantics/published

Epilepsy is a common condition in the United States. It is estimated that 1.2% of the population or 3.4 million people have epilepsy. This figure may be underestimated because of potential repercussions and stigma in disclosing epilepsy.1 Studies have shown that people with epilepsy are more likely to be unemployed or unable to work, have lower annual household incomes, be obese and physically inactive, and be less likely to marry.2,3 People with epilepsy can have poor overall health status, impaired intellectual and physical functioning, elevated risk of accidents and injuries, and negative side effects from antiseizure medications.2,3 It is estimated the annual direct medical cost of epilepsy in the United States is $9.6 billion; combined with indirect costs, the total rises to $15.5 billion yearly.2

ZusammenfassungEine 2013 von der „International Federation of Clinical Neurophysiology“ gegründete Taskforce hat eine international konsensfähige EEG-Terminologie entwickelt. Im Folgenden soll das Resultat – die 2. Version des Standardized Computer-based Organized Reporting of EEG (SCORE) - vorgestellt werden. Die Terminologie wurde im Rahmen eines Softwarepaketes (SCORE-EEG) in der klinischen Praxis an über 12.000 EEGs getestet. Die Auswahl der Begriffe ist kontextabhängig: die initiale Auswahl bestimmt, welche weiteren Auswahlmöglichkeiten zur Verfügung stehen. Im Verlauf wird automatisch ein Befund erstellt und dessen Einzelmerkmale in eine Datenbank eingespeist. SCORE verfügt über Module spezifisch für die Befundung epileptischer Anfälle, sowie charakteristischer neonataler und intensivmedizinische EEG-Merkmale. SCORE ist nicht nur ein nützliches Werkzeug im ambulanten, klinischen und wissenschaftlichen Setting, es erleichtert auch Qualitätssicherung, Datenaustausch und die EEG-Aus und Weiterbildung.

Acute repetitive seizures and status epilepticus (SE) are common neurologic emergencies with high morbidity and mortality (Zaccara et al., 2017). Most epileptic seizures are stopped within seconds to minutes by intrinsic seizure terminating mechanisms (Theodore et al., 1994). Individual seizures are usually followed by a refractory period, during which additional seizures do not occur. Failure of the normal seizure terminating mechanisms or the postictal refractory period leads to seizure emergencies. Prolonged or repetitive seizures can lead to neuronal damage and clinical sequelae such as focal neurological deficits, cognitive and motor impairments, and epilepsy. Acute seizure emergencies fall into several subgroups based on the seizure type, duration of seizures, etiology, electroencephalographic patterns, and age. Clinical trials to address acute seizures can have one of several purposes: (1) rapid termination of prolonged or repetitive seizures before SE occurs; (2) termination of established SE; (3) termination of refractory SE (RSE); or (4) amelioration of neuronal damage induced by prolonged seizures (neuroprotection), to prevent long-term sequelae such as cognitive dysfunction and epilepsy (antiepileptogenesis). Recently completed randomized clinical trials (Alldredge et al., 2001; Treiman et al., 1998; Silbergleit et al., 2012) provide some guidelines for aims (1) and (2), but available treatments for seizure emergencies and SE are effective in terminating seizures in only about 60% of patients. Treatment options for patients not responding to initial therapy are controversial. No clinical trials have yet addressed neuroprotection or antiepileptogenesis after acute seizures and SE in humans. This chapter will review the epidemiology, pathophysiology, current management, and trial design methodology for acute prolonged or repetitive seizures and SE.

The clinical presentation and encephalographic (EEG) findings of nonconvulsive status epilepticus (NCSE) can be complicated, making diagnosis difficult. There are generalized (e.g., absence status) and focal (e.g., aphasic status, complex partial status) forms. Some patients are responsive but have cognitive or other neurologic deficits; others are less responsive or even comatose. Increasingly, the diagnosis of NCSE is considered in intensive care unit patients. Here, without clinical signs of seizures such as convulsions, EEG is critical in diagnosis, but there is uncertainty about which EEG patterns represent seizures and which clinical situations and EEG patterns warrant aggressive treatment. Antiseizure medications are tailored to the NCSE type and the clinical condition. Treatment is often easier for NCSE, and the outcome better, than for convulsive SE, but this is not always true for critically ill patients with NCSE in the ICU, for whom continuous EEG monitoring is often crucial for diagnosis and management.

Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted in the second, revised version of SCORE (Standardized Computer-based Organized Reporting of EEG), which is presented in this paper. The revised terminology was implemented in a software package (SCORE EEG), which was tested in clinical practice on 12,160 EEG recordings. Standardized terms implemented in SCORE are used to report the features of clinical relevance, extracted while assessing the EEGs. Selection of the terms is context sensitive: initial choices determine the subsequently presented sets of additional choices. This process automatically generates a report and feeds these features into a database. In the end, the diagnostic significance is scored, using a standardized list of terms. SCORE has specific modules for scoring seizures (including seizure semiology and ictal EEG patterns), neonatal recordings (including features specific for this age group), and for Critical Care EEG Terminology. SCORE is a useful clinical tool, with potential impact on clinical care, quality assurance, data-sharing, research and education.

This chapter reviews the technical aspects of recording and reviewing clinical electroencephalograms (EEGs) and related biopotentials. While advances in engineering technology have revolutionized EEG machines, the basic principles underlying accurate representation of brain activity are largely unchanged. The first section reviews the analog EEG components, and the second section discusses analog-to-digital conversion, digital filters, and display and storage parameters. Digital EEG machines are now less expensive and their capabilities far surpass those of analog machines. The third section reviews how electrode positions and systems of signal display (montages) can be used to determine the polarity and field of EEG signals. The final section describes how other biopotentials are acquired and displayed. Polygraphy can provide crucial information on other physiological processes that can impact EEG activity and can help identify potential artifactual signals. We highlight recent advances that allow the recording of a broader range of EEG frequencies and spatial distribution.

Generalized convulsive status epilepticus (GCSE) is a serious neurologic illness causing unresponsiveness, major physiologic disturbances, risk of injury and, if prolonged enough, neuronal damage. Causes are many, and the outcome often depends as much on the etiology as on the epileptic seizure itself. Several anti-seizure medications are used in treatment of GCSE, but some cases continue electrographically when clinical convulsions cease (nonconvulsive SE), and EEG is essential in their diagnosis. About 20% of cases become refractory to initial treatment, and the EEG becomes even more crucial in diagnosis and management. This chapter also covers other forms of SE with significant motor manifestations including: focal motor status (including epilepsia partialis continua); myoclonic status, which includes some relatively benign forms as well as some with a very poor prognosis; and clonic and tonic status. It reviews the many different EEG findings in those forms of status, and the use of EEG in their treatment and management, especially in prolonged cases.

Although the epilepsy and neurology communities have position papers on a number of topics pertaining to epilepsy diagnosis and management, no current paper exists for the rationale and appropriate indications for epilepsy monitoring unit (EMU) evaluation. General neurologists, hospital administrators, and insurers also have yet to fully understand the role this type of testing has in the diagnosis and management of individuals with paroxysmal neurologic symptoms. This review outlines the indications for long-term video-electroencephalography (VEEG) for typical elective admissions to a specialized inpatient setting. The common techniques used in EMUs to obtain diagnostic information are reviewed. The added benefit of safety measures and clinical testing above that available for routine or long-term ambulatory electroencephalography is also discussed. The indications for admission to the EMU include differential diagnosis of paroxysmal spells, characterization of seizure types, presurgical epilepsy evaluations, seizure quantification, monitoring medication adjustment in a safe setting, and differentiation between seizures and side effects. We conclude that the appropriate use of this specialized testing can lead to an early and correct diagnosis in a variety of clinical circumstances. The EMU evaluation is considered the gold standard test for the definitive diagnosis of epilepsy and seizure-like spells.

Summary Objective Continuous EEG monitoring (cEEG) of critically ill adults is being used with increasing frequency, and practice guidelines on indications for cEEG monitoring have recently been published. However, data describing the current practice of cEEG in critically ill adults is limited. We aimed to describe the current practice of cEEG monitoring in adults in the United States. Methods A survey assessing cEEG indications and procedures was sent to one intensivist and one neurophysiologist responsible for intensive care unit (ICU) cEEG at 151 institutions in the United States. At some institutions only one physician could be identified. Results One hundred thirty-seven physicians from 97 institutions completed the survey. Continuous EEG is utilized by nearly all respondents to detect nonconvulsive seizures (NCS) in patients with altered mental status following clinical seizures, intra cerebral hemorrhage (ICH), traumatic brain injury, and cardiac arrest, as well as to characterize abnormal movements suspected to be seizures. The majority of physicians monitor comatose patients for 24–48 h. In an ideal situation with unlimited resources, 18% of respondents would increase cEEG duration. Eighty-six percent of institutions have an on-call EEG technologist available 24/7 for new patient hookups, but only 26% have technologists available 24/7 in-house. There is substantial variability in who reviews EEG and how frequently it is reviewed as well as use of quantitative EEG. Significance Although there is general agreement regarding the indications for ICU cEEG, there is substantial interinstitutional variability in how the procedure is performed.

Since its original description in 2007, anti-N-methyl-d-aspartate receptor (anti-NMDAR) encephalitis associated with an ovarian teratoma is an increasingly recognized etiology of previously unexplained encephalopathy and encephalitis. Extreme delta brush (EDB) is a novel electroencephalogram (EEG) finding seen in many patients with anti-NMDAR encephalitis. The presence of this pattern is associated with a more prolonged illness, although the specificity of this pattern is unclear. Additionally, the frequency and sensitivity of EDB in anti-NMDAR encephalitis and its implications for outcome have yet to be determined. We report a patient with early evidence of extreme delta brush and persistence of this pattern 17.5weeks later with little clinical improvement.

Introduction High density EEG (hdEEG) systems and Electrical Source Imaging (ESI) techniques have revolutionized our ability to assess the potential sources of epileptiform activity and other EEG features. HdEEG interpretation is limited by lack of standardized electrode position nomenclature and montages. Inefficient visual review of hdEEG remains a major barrier to incorporating these techniques into clinical care. The aim of this project is to propose an electrode map and nomenclature with digitized electrode locations for easy integration with neuroimaging studies and ESI. Methods The 256 channel EEG net (Geodesic Sensor Net, Electrical Geodesics, Inc.) includes electrode locations approximating the International 10–20 and 10–10 systems, but intervening electrodes do not have a standardized location, and many electrodes over the cheeks and face do not have positions defined by the 10–10 or 10–5 systems. We co-registered average 256 channel EEG net electrode positions to the 10–10 positions based on the Montreal Neurologic Institute using Curry 7.0 software. then clustered the 256 electrode locations to their closest 10–10 electrodes tested several channel layouts (montages) allowing rapid transition from global views (all head regions) to focal views (subregions corresponding to 10–10 electrode coordinates). Results 256 electrode locations were successfully clustered around their closest 10–10 electrode, deemed its cardinal point. All 256 electrodes within a cluster received the same initial letters according to the 10–10 electrode at its center. Within a cluster, the first subscript identified its position either anterior (a) or posterior (p) to the cardinal 10–10 electrode. The second subscript identified its position either superior (s) or inferior (i) to the cardinal 10–10 electrode. The last subscript, numerical, identified the proximity of the nonstandard electrode relative to the cardinal 10–10 electrode, compared to the other nonstandard electrodes in that cluster. Therefore, the closest nonstandard electrode would have the subscript as 1, and each subscript would increase in numeric value as distance from the cardinal 10–10 electrode increases. Conclusion Electrode position nomenclature which builds upon the international standard 10–10 system allows electroencephalographers to identify spatial areas of interest in hdEEG relative to positions in routine use. A standard viewing montage for hdEEG and its application with ESI both boosts efficiency when reviewing data, and also improves accuracy in recognizing epileptiform discharges. Furthermore, combining the montage with electrode geopositions will provide greater continuity and ease of use across analysis techniques that would potentially increase adoption of source analysis in clinical care. Our proposed system could also be expanded to higher numbers of electrodes or additional positions on the face or head and is not limited to a specific hdEEG system or electrode count.

PurposeTo examine the efficacy and safety profile of antiepileptic repetitive transcranial magnetic stimulation (rTMS) for refractory status epilepticus (RSE) in the intensive care unit (ICU) setting. In addition, hypothetical concerns about electrical interference of rTMS with ICU equipment have been previously raised.MethodsWe describe two cases of RSE treated with rTMS in the ICU.ResultsIn one case, rTMS contributed to decreased seizure frequency; in the second case, rTMS transiently decreased seizure frequency. In both cases, rTMS was safe and did not interfere with the functioning of the ICU equipment.ConclusionrTMS is a potential therapy for RSE when conventional therapies have failed. Future studies should investigate the efficacy of various rTMS stimulation parameters, safety issues, and bioengineering considerations in the ICU setting.

Continuous EEG Monitoring is becoming a commonly used tool in assessing brain function in critically ill patients. However, there is no uniformly accepted nomenclature for EEG patterns frequently encountered in these patients such as periodic discharges, fluctuating rhythmic patterns, and combinatio

Purpose An NINDS-sponsored conference in April of 2011 reviewed issues in epilepsy clinical trials. One goal was to clarify new electronic methods for recording seizure information and other data in clinical trials. Methods This selective literature review and compilation of expert opinion considers advantages and limitations of traditional paper-based seizure diaries in comparison to electronic diaries. Key findings Seizure diaries are a type of patient-reported outcome. All seizure diaries depend first on accurate recognition and recording of seizures, which is a problem since about half of seizures recorded during video-EEG monitoring are not known to the patient. Reliability of recording is another key issue. Diaries may not be at hand after a seizure, lost or not brought to clinic visits. On-line electronic diaries have several potential advantages over paper diaries. Smartphones are increasingly accessible as data entry gateways. Data are not easily lost and are accessible from clinic. Entries can be time-stamped and provide immediate feedback, validation or reminders. Data can also can be graphed and pasted into an EMR. Disadvantages include need for digital sophistication, higher cost, increased setup time, and requiring attention to potential privacy issues. The Epilepsy Diary by epilepsy.com and Irody, Inc. has over 13,000 registrants and SeizureTracker over 10,000, and both are used for clinical and research purposes. Some studies have documented patient preference and increased compliance for electronic versus paper diaries. Seizure diaries can be challenging in the pediatric population. Children often have multiple seizure types and limited reporting of subjective symptoms. Multiple caregivers during the day require more training to produce reliable and consistent data. Diary-based observational studies have the advantages of low cost, allowing locus-of-control by the patient and testing in a “real-world” environment. Diary-based studies can also be useful as descriptive “snapshots” of a population. However, the type of information available is very different from that obtained by prospective controlled studies. The act of self-recording observations may itself influence the observation, for example, by causing the subject to attend more vigilantly to seizures after changing medication. Pivotal anti-seizure drug or device trials still mostly rely on paper-based seizure diaries. Industry is aware of the potential advantages of electronic diaries, particularly, the promise of real-time transmission of data, time-stamping of entries, reminders to subjects, and potentially automatic interfaces to other devices. However, until diaries are validated as research tools and the regulatory environment becomes clearer, adoption of new types of diaries as markers for a primary study outcome will be cautious. Significance Recommendations from the conference included: further studies of validity of epilepsy diaries and how they can be used to improve adherence; use and further development of core data sets, such as the one recently developed by NINDS; encouraging links of diaries to electronic sensors; development of diary privacy and legal policies; examination of special pediatric diary issues; development of principles for observational research from diaries; and work with the FDA to make electronic diaries more useful in industry-sponsored clinical trials.

Early myoclonus in comatose survivors of cardiac arrest, even when it is not myoclonic status epilepticus (MSE), is considered a sign of severe global brain ischemia and has been associated with high rates of mortality and poor neurologic outcomes. We report on three survivors of primary circulatory cardiac arrests who had good neurologic outcomes (two patients with a CPC score=1 and one patient with a CPC score=2) after mild therapeutic hypothermia, despite exhibiting massive myoclonus within the first 4h after return of spontaneous circulation. The concept that early myoclonus heralds a uniformly poor prognosis may need to be reconsidered in the era of post-cardiac arrest mild therapeutic hypothermia.

This document was developed by the members of the Committee to Revise the Guidelines for Services, Personnel, and Facilities at Specialized Epilepsy Centers. After discussions with the general membership they were adopted by the Board of the National Association of Epilepsy Centers. The Guidelines will be reviewed and updated when considered necessary by the Board.

Introduction Despite major advances in using high density EEG (hdEEG) and electrical source imaging (ESI) for analysis of interictal activity, research demonstrating its utility in examining ictal activity is still lacking. This may be a result of technical challenges associated with capturing epileptic seizures on scalp hdEEG recordings (physical constraints, such as high number of sensors, lack of standardization in review, comfort, etc.). Furthermore, the value of using interictal discharges as a valid estimate of seizure onset zone (SOZ) still remains disputable. The aim of this study is to use electrical source imaging (ESI) to evaluate the co-localization of ictal and interictal activity through the use of high density scalp EEG. Methods A retrospective analysis was performed on 5 patients with intractable focal temporal and extratemporal lobe epilepsy. EEG scalp recordings were obtained using high density 256 Geodesic Sensory Net (Geodesic Sensor Net, Electrical Geodesic Inc.). ESI was performed using two different commercially available ESI systems, Curry 7.0 (Compumedics) and MIPEGI (Electrical Geodesic Inc.). Sensor locations were determined using the Geodesic Photogrammetry System (GPS) and Geosource 3 (Electrical Geodesic Inc), Realistic geometric individualized head models were constructed from patient specific MRIs using the boundary element model and the finite difference model. Source reconstruction was based on distributed source models and dipole models. The concordance of irritative and ictal onset zones was estimated by the distance between maximal ictal and interictal source activity. Results ESI identified the seizure onset and irritative zones in all 5 patients. There was good concordance between the estimated seizure onset zone and irritative zone on the sub-lobar level. Similar results were obtained through the use of the boundary element and finite difference models. Conclusion The results provide insight into whether interictal discharges provide accurate estimate of SOZ. Furthermore, these results assess similarity of source reconstruction obtained through two different types of individual head models that are commercially available.