On-demand EEG education through competition - A novel, app-based approach to learning to identify interictal epileptiform discharges.

Objective: Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG. Methods: We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings. Results: Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice. Conclusions: Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback. Significance: This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation.

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.

Epilepsy diagnosis using a clinical decision tool and artificially intelligent electroencephalography.

OBJECTIVE: To construct a tool for non-experts to calculate the probability of epilepsy based on easily obtained clinical information combined with an artificial intelligence readout of the electroencephalogram (AI-EEG). MATERIALS AND METHODS: We performed a chart review of 205 consecutive patients aged 18 years or older who underwent routine EEG. We created a point system to calculate the pre-EEG probability of epilepsy in a pilot study cohort. We also computed a post-test probability based on AI-EEG results. RESULTS: One hundred and four (50.7%) patients were female, the mean age was 46 years, and 110 (53.7%) were diagnosed with epilepsy. Findings favoring epilepsy included developmental delay (12.6% vs 1.1%), prior neurological injury (51.4% vs 30.9%), childhood febrile seizures (4.6% vs 0.0%), postictal confusion (43.6% vs 20.0%), and witnessed convulsions (63.6% vs 21.1%); findings favoring alternative diagnoses were lightheadedness (3.6% vs 15.8%) or onset after prolonged sitting or standing (0.9% vs 7.4%). The final point system included 6 predictors: Presyncope (-3 points), cardiac history (-1), convulsion or forced head turn (+3), neurological disease history (+2), multiple prior spells (+1), postictal confusion (+2). Total scores of ≤1 point predicted <5% probability of epilepsy, while cumulative scores ≥7 predicted >95%. The model showed excellent discrimination (AUROC: 0.86). A positive AI-EEG substantially increases the probability of epilepsy. The impact is greatest when the pre-EEG probability is near 30%. SIGNIFICANCE: A decision tool using a small number of historical clinical features accurately predicts the probability of epilepsy. In indeterminate cases, AI-assisted EEG helps resolve uncertainty. This tool holds promise for use by healthcare workers without specialty epilepsy training if validated in an independent cohort.

Routine and sleep EEG: Minimum recording standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy.

This article provides recommendations on the minimum standards for recording routine ("standard") and sleep electroencephalography (EEG). The joint working group of the International Federation of Clinical Neurophysiology (IFCN) and the International League Against Epilepsy (ILAE) developed the standards according to the methodology suggested for epilepsy-related clinical practice guidelines by the Epilepsy Guidelines Working Group. We reviewed the published evidence using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. The quality of evidence for sleep induction methods was assessed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) method. A tool for Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the risk of bias in technical and methodological studies. Where high-quality published evidence was lacking, we used modified Delphi technique to reach expert consensus. The GRADE system was used to formulate the recommendations. The quality of evidence was low or moderate. We formulated 16 consensus-based recommendations for minimum standards for recording routine and sleep EEG. The recommendations comprise the following aspects: indications, technical standards, recording duration, sleep induction, and provocative methods.

Routine and sleep EEG: Minimum recording standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy.

This article provides recommendations on the minimum standards for recording routine ("standard") and sleep electroencephalography (EEG). The joint working group of the International Federation of Clinical Neurophysiology (IFCN) and the International League Against Epilepsy (ILAE) developed the standards according to the methodology suggested for epilepsy-related clinical practice guidelines by the Epilepsy Guidelines Working Group. We reviewed the published evidence using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. The quality of evidence for sleep induction methods was assessed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) method. A tool for Quality Assessment of Diagnostic Studies (QUADAS-2) was used to assess the risk of bias in technical and methodological studies. Where high-quality published evidence was lacking, we used modified Delphi technique to reach expert consensus. The GRADE system was used to formulate the recommendations. The quality of evidence was low or moderate. We formulated 16 consensus-based recommendations for minimum standards for recording routine and sleep EEG. The recommendations comprise the following aspects: indications, technical standards, recording duration, sleep induction, and provocative methods.

Mood and Anxiety Disorders and Suicidality in Patients With Newly Diagnosed Focal Epilepsy: An Analysis of a Complex Comorbidity.

BACKGROUND AND OBJECTIVES: Mood, anxiety disorders, and suicidality are more frequent in people with epilepsy than in the general population. Yet, their prevalence and the types of mood and anxiety disorders associated with suicidality at the time of the epilepsy diagnosis are not established. We sought to answer these questions in patients with newly diagnosed focal epilepsy and to assess their association with suicidal ideation and attempts. METHODS: The data were derived from the Human Epilepsy Project study. A total of 347 consecutive adults aged 18-60 years with newly diagnosed focal epilepsy were enrolled within 4 months of starting treatment. The types of mood and anxiety disorders were identified with the Mini International Neuropsychiatric Interview, whereas suicidal ideation (lifetime, current, active, and passive) and suicidal attempts (lifetime and current) were established with the Columbia Suicidality Severity Rating Scale (CSSRS). Statistical analyses included the t test, χ2 statistics, and logistic regression analyses. RESULTS: A total of 151 (43.5%) patients had a psychiatric diagnosis; 134 (38.6%) met the criteria for a mood and/or anxiety disorder, and 75 (21.6%) reported suicidal ideation with or without attempts. Mood (23.6%) and anxiety (27.4%) disorders had comparable prevalence rates, whereas both disorders occurred together in 43 patients (12.4%). Major depressive disorders (MDDs) had a slightly higher prevalence than bipolar disorders (BPDs) (9.5% vs 6.9%, respectively). Explanatory variables of suicidality included MDD, BPD, panic disorders, and agoraphobia, with BPD and panic disorders being the strongest variables, particularly for active suicidal ideation and suicidal attempts. DISCUSSION: In patients with newly diagnosed focal epilepsy, the prevalence of mood, anxiety disorders, and suicidality is higher than in the general population and comparable to those of patients with established epilepsy. Their recognition at the time of the initial epilepsy evaluation is of the essence.

Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs.

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 ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: 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.

Minimum Technical Requirements for Performing Ambulatory EEG.

SUMMARY: Ambulatory EEG (AEEG) devices offer portable, multichannel, digital EEG recording with or without video in the patient's natural environment. The technology applied for AEEG recording is like the technology for routine EEG and inpatient long-term video-EEG monitoring but designed to be compact and wearable. Computer-based AEEG technology is well-suited to digital recording, signal processing, and visual display. However, acquiring interpretable EEG outside of the hospital setting presents its own technical challenges. Published guidelines have established technical standards for performing routine EEG and inpatient video-EEG monitoring, but technical standards for AEEG are lacking. Therefore, this guideline provides minimal technical standards for the performance of AEEG which are essential to ensure the quality of studies for clinical and research practice. We expect these minimum standards to evolve over time with improved performance and advances in the technology.

Measuring expertise in identifying interictal epileptiform discharges.

OBJECTIVE: Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. METHODS: A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. RESULTS: Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE: Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.

Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology.

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility.

Long-term individual retention with cenobamate in adults with focal seizures: Pooled data from the clinical development program.

OBJECTIVE: We determined retention on open-label cenobamate therapy in the clinical development program to assess the long-term efficacy and tolerability of adjunctive cenobamate in individuals with uncontrolled focal seizures. METHODS: Data from two randomized, controlled cenobamate studies and one open-label safety and pharmacokinetic study were pooled. Based on the percentage of participants remaining on treatment, retention rates were estimated using Kaplan-Meier survival analyses. We performed two additional analyses to assess factors contributing to retention, stratifying a robust data set (through 2 years) by cenobamate modal dose and frequently used concomitant anti-seizure medications. Cenobamate discontinuations and treatment-emergent adverse events were summarized. RESULTS: Data from 1844 participants were pooled: 149 from a single-dose randomized trial, 355 from a multi-dose randomized trial, and 1340 from an open-label safety and pharmacokinetic study. Most participants from randomized trials continued in open-label extensions, and pooled data represent >95% of participants exposed to cenobamate. Baseline characteristics and disease and treatment histories were similar across studies. Median duration of cenobamate exposure was 34 months, with a median modal dose of 200 mg/day. Kaplan-Meier estimates of cumulative cenobamate retention rates were 80% at 1 year and 72% at 2 years. Once participants reached the maintenance phase, retention rates were consistently high in participants receiving ≥100 mg/day cenobamate, and concomitant anti-seizure medications did not affect long-term retention. By 2 years, 535 (29%) had actually discontinued cenobamate; the most common reasons for discontinuation were adverse events (37.6%), withdrawal of consent (21.1%), and other (16.8%). SIGNIFICANCE: Treatment retention rates provide a proxy measure for long-term efficacy, safety, tolerability, and adherence. The consistently high retention rates we found suggest that cenobamate may be an effective and well-tolerated new treatment option for people with drug-resistant focal seizures.

Minimum standards for inpatient long-term video-electroencephalographic monitoring: A clinical practice guideline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology.

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and to establish its clinical utility.

Safety and Efficacy of Natalizumab as Adjunctive Therapy for People With Drug-Resistant Epilepsy: A Phase 2 Study.

BACKGROUND AND OBJECTIVES: To explore efficacy/safety of natalizumab, a humanized monoclonal anti-α4-integrin antibody, as adjunctive therapy in adults with drug-resistant focal epilepsy. METHODS: Participants with ≥6 seizures during the 6-week baseline period were randomized 1:1 to receive natalizumab 300 mg IV or placebo every 4 weeks for 24 weeks. Primary efficacy outcome was change from baseline in log-transformed seizure frequency, with a predefined threshold for therapeutic success of 31% relative reduction in seizure frequency over the placebo group. Countable seizure types were focal aware with motor signs, focal impaired awareness, and focal to bilateral tonic-clonic. Secondary efficacy endpoints/safety were also assessed. RESULTS: Of 32 and 34 participants dosed in the natalizumab 300 mg and placebo groups, 30 (94%) and 31 (91%) completed the placebo-controlled treatment period, respectively (one participant was randomized to receive natalizumab but not dosed due to IV complications). Estimated relative change in seizure frequency of natalizumab over placebo was -14.4% (95% confidence interval [CI] -46.1%-36.1%; p = 0.51). The proportion of participants with ≥50% reduction from baseline in seizure frequency was 31.3% for natalizumab and 17.6% for placebo (odds ratio 2.09, 95% CI 0.64-6.85; p = 0.22). Adverse events were reported in 24 (75%) and 22 (65%) participants receiving natalizumab vs placebo. DISCUSSION: Although the threshold to demonstrate efficacy was not met, there were no unexpected safety findings and further exploration of possible anti-inflammatory therapies for drug-resistant epilepsy is warranted. TRIAL REGISTRATION INFORMATION: The ClinicalTrials.gov registration number is NCT03283371. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that IV natalizumab every 4 weeks, compared to placebo, did not significantly change seizure frequency in adults with drug-resistant epilepsy. The study lacked the precision to exclude an important effect of natalizumab.

Add-on cannabidiol in patients with Dravet syndrome: Results of a long-term open-label extension trial.

OBJECTIVE: Add-on cannabidiol (CBD) reduced seizures associated with Dravet syndrome (DS) in two randomized, double-blind, placebo-controlled trials: GWPCARE1 Part B (NCT02091375) and GWPCARE2 (NCT02224703). Patients who completed GWPCARE1 Part A (NCT02091206) or Part B, or GWPCARE2, were enrolled in a long-term open-label extension trial, GWPCARE5 (NCT02224573). We present an interim analysis of the safety, efficacy, and patient-reported outcomes from GWPCARE5. METHODS: Patients received a pharmaceutical formulation of highly purified CBD in oral solution (100 mg/ml), titrated from 2.5 to 20 mg/kg/day over a 2-week period, added to their existing medications. Based on response and tolerance, CBD could be reduced or increased to 30 mg/kg/day. RESULTS: Of the 330 patients who completed the original randomized trials, 315 (95%) enrolled in this open-label extension. Median treatment duration was 444 days (range = 18-1535), with a mean modal dose of 22 mg/kg/day; patients received a median of three concomitant antiseizure medications. Adverse events (AEs) occurred in 97% patients (mild, 23%; moderate, 50%; severe, 25%). Commonly reported AEs were diarrhea (43%), pyrexia (39%), decreased appetite (31%), and somnolence (28%). Twenty-eight (9%) patients discontinued due to AEs. Sixty-nine (22%) patients had liver transaminase elevations >3 × upper limit of normal; 84% were on concomitant valproic acid. In patients from GWPCARE1 Part B and GWPCARE2, the median reduction from baseline in monthly seizure frequency assessed in 12-week periods up to Week 156 was 45%-74% for convulsive seizures and 49%-84% for total seizures. Across all visit windows, ≥83% patients/caregivers completing a Subject/Caregiver Global Impression of Change scale reported improvement in overall condition. SIGNIFICANCE: We show that long-term CBD treatment had an acceptable safety profile and led to sustained, clinically meaningful reductions in seizure frequency in patients with treatment-resistant DS.

Long-term safety and efficacy of add-on cannabidiol in patients with Lennox-Gastaut syndrome: Results of a long-term open-label extension trial.

OBJECTIVE: Lennox-Gastaut syndrome (LGS) is an epileptic encephalopathy that is often treatment resistant. Efficacy and safety of add-on cannabidiol (CBD) to treat seizures associated with LGS was demonstrated in two randomized controlled trials (RCTs). Patients who completed the RCTs were invited to enroll in this long-term open-label extension (OLE) trial, GWPCARE5 (NCT02224573). We present the final analysis of safety and efficacy outcomes from GWPCARE5. METHODS: Patients received plant-derived highly purified CBD (Epidiolex in the United States; Epidyolex in the European Union; 100 mg/ml oral solution), titrated to a target maintenance dose of 20 mg/kg/day over 2 weeks. Based on response and tolerability, CBD could then be reduced or increased up to 30 mg/kg/day. RESULTS: Of 368 patients with LGS who completed the RCTs, 366 (99.5%) enrolled in this OLE. Median and mean treatment duration were 1090 and 826 days (range = 3-1421), respectively, with a mean modal dose of 24 mg/kg/day. Adverse events (AEs) occurred in 96% of patients, serious AEs in 42%, and AE-related discontinuations in 12%. Common AEs were convulsion (39%), diarrhea (38%), pyrexia (34%), and somnolence (29%). Fifty-five (15%) patients experienced liver transaminase elevations more than three times the upper limit of normal; 40 (73%) were taking concomitant valproic acid. Median percent reductions from baseline ranged 48%-71% for drop seizures and 48%-68% for total seizures through 156 weeks. Across all 12-week visit windows, 87% or more of patients/caregivers reported improvement in the patient's overall condition on the Subject/Caregiver Global Impression of Change scale. SIGNIFICANCE: Long-term add-on CBD treatment had a similar safety profile as in the original RCTs. Sustained reductions in drop and total seizure frequency were observed for up to 156 weeks, demonstrating long-term benefits of CBD treatment for patients with LGS.

A phase 1b/2a study of soticlestat as adjunctive therapy in participants with developmental and/or epileptic encephalopathies.

OBJECTIVE: To evaluate the safety, tolerability, and pharmacokinetics of soticlestat, a first-in-class cholesterol 24-hydroxylase inhibitor, in adults with developmental and/or epileptic encephalopathies (DEE). METHODS: The study comprised a 30-day, randomized, double-blind, placebo-controlled phase (Part A), followed by a 55-day open-label phase (Part B) (ClinicalTrials.gov ID: NCT03166215) . In Part A, patients with DEE and at least one bilateral motor seizure during the 4-week prospective baseline period were randomized 4:1 to receive soticlestat or placebo, in addition to their usual antiseizure medication. In Part B, all patients received open-label soticlestat. Soticlestat doses were titrated according to tolerability to a maximum of 300 mg twice daily (BID). Safety evaluations included the incidence of treatment-emergent adverse events (TEAEs). Plasma soticlestat concentrations were measured at various times for determination of multiple-dose pharmacokinetics and 24S-hydroxycholesterol (24HC). Efficacy was assessed by evaluation of changes in seizure frequency from baseline. RESULTS: Eighteen patients (median age, 28.5 years) were enrolled and randomized, and 14 (78 %) completed the study. In Part A, TEAEs occurred in 71.4 % of soticlestat-treated patients and 100 % of placebo-treated patients. In Part B, the overall incidence of TEAEs was 68.8 %. In Part A, TEAEs that occurred in more than one patient in the soticlestat group were dysarthria (n = 3, 21.4 %), lethargy (n = 2, 14.3 %), upper respiratory tract infection (n = 2, 14.3 %), fatigue (n = 2, 14.3 %), and headache (n = 2, 14.3 %). Four patients discontinued treatment because of TEAEs, of whom two reported drug-related seizure clusters as serious TEAEs. There were no deaths. Pharmacokinetic analysis showed dose-dependent increases in systemic exposure and peak plasma soticlestat concentrations. At the end of Part B, the overall mean percent change from baseline in plasma 24HC was -80.97 %. Changes from baseline in median seizure frequency were +16.71 % and +22.16 % in the soticlestat and placebo groups, respectively, in Part A, and -36.38 % in all participants in Part B. CONCLUSION: Soticlestat was well tolerated at doses of up to 300 mg BID and was associated with a reduction in median seizure frequency over the study duration. Further studies are warranted to assess the possible efficacy of soticlestat as adjunctive therapy in patients with DEEs such as Dravet syndrome and Lennox-Gastaut syndrome.

Standardization of neurophysiology signal data into the DICOM® standard.

A standard format for neurophysiology data is urgently needed to improve clinical care and promote research data exchange. Previous neurophysiology format standardization projects have provided valuable insights into how to accomplish the project. In medical imaging, the Digital Imaging and Communication in Medicine (DICOM) standard is widely adopted. DICOM offers a unique environment to accomplish neurophysiology format standardization because neurophysiology data can be easily integrated with existing DICOM-supported elements such as video, ECG, and images and also because it provides easy integration into hospital Picture Archiving and Communication Systems (PACS) long-term storage systems. Through the support of the International Federation of Clinical Neurophysiology (IFCN) and partners in industry, DICOM Working Group 32 (WG-32) has created an initial set of standards for routine electroencephalography (EEG), polysomnography (PSG), electromyography (EMG), and electrooculography (EOG). Longer and more complex neurophysiology data types such as high-definition EEG, long-term monitoring EEG, intracranial EEG, magnetoencephalography, advanced EMG, and evoked potentials will be added later. In order to provide for efficient data compression, a DICOM neurophysiology codec design competition will be held by the IFCN and this is currently being planned. We look forward to a future when a common DICOM neurophysiology data format makes data sharing and storage much simpler and more efficient.

Deep active learning for Interictal Ictal Injury Continuum EEG patterns.

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.

Automated Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms by Convolutional Neural Networks.

Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges (IEDs) as distinctive biomarkers of epilepsy has various limitations, including time-consuming reviews, steep learning curves, interobserver variability, and the need for specialized experts. The development of an automated IED detector is necessary to provide a faster and reliable diagnosis of epilepsy. In this paper, we propose an automated IED detector based on Convolutional Neural Networks (CNNs). We have evaluated the proposed IED detector on a sizable database of 554 scalp EEG recordings (84 epileptic patients and 461 nonepileptic subjects) recorded at Massachusetts General Hospital (MGH), Boston. The proposed CNN IED detector has achieved superior performance in comparison with conventional methods with a mean cross-validation area under the precision-recall curve (AUPRC) of 0.838[Formula: see text]±[Formula: see text]0.040 and false detection rate of 0.2[Formula: see text]±[Formula: see text]0.11 per minute for a sensitivity of 80%. We demonstrated the proposed system to be noninferior to 30 neurologists on a dataset from the Medical University of South Carolina (MUSC). Further, we clinically validated the system at National University Hospital (NUH), Singapore, with an agreement accuracy of 81.41% with a clinical expert. Moreover, the proposed system can be applied to EEG recordings with any arbitrary number of channels.