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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation

Importance

Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability.

Objective

To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs.

Design, Setting, and Participants

A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built.

Main Outcomes and Measures

SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation.

Results

SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865).

Conclusions and Relevance

In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

Interrater Reliability of Experts in Identifying Interictal Epileptiform Discharges in Electroencephalograms

Importance

The validity of using electroencephalograms (EEGs) to diagnose epilepsy requires reliable detection of interictal epileptiform discharges (IEDs). Prior interrater reliability (IRR) studies are limited by small samples and selection bias.

Objective

To assess the reliability of experts in detecting IEDs in routine EEGs.

Design, Setting, and Participants

This prospective analysis conducted in 2 phases included as participants physicians with at least 1 year of subspecialty training in clinical neurophysiology. In phase 1, 9 experts independently identified candidate IEDs in 991 EEGs (1 expert per EEG) reported in the medical record to contain at least 1 IED, yielding 87 636 candidate IEDs. In phase 2, the candidate IEDs were clustered into groups with distinct morphological features, yielding 12 602 clusters, and a representative candidate IED was selected from each cluster. We added 660 waveforms (11 random samples each from 60 randomly selected EEGs reported as being free of IEDs) as negative controls. Eight experts independently scored all 13 262 candidates as IEDs or non-IEDs. The 1051 EEGs in the study were recorded at the Massachusetts General Hospital between 2012 and 2016.

Main Outcomes and Measures

Primary outcome measures were percentage of agreement (PA) and beyond-chance agreement (Gwet κ) for individual IEDs (IED-wise IRR) and for whether an EEG contained any IEDs (EEG-wise IRR). Secondary outcomes were the correlations between numbers of IEDs marked by experts across cases, calibration of expert scoring to group consensus, and receiver operating characteristic analysis of how well multivariate logistic regression models may account for differences in the IED scoring behavior between experts.

Results

Among the 1051 EEGs assessed in the study, 540 (51.4%) were those of females and 511 (48.6%) were those of males. In phase 1, 9 experts each marked potential IEDs in a median of 65 (interquartile range [IQR], 28-332) EEGs. The total number of IED candidates marked was 87 636. Expert IRR for the 13 262 individually annotated IED candidates was fair, with the mean PA being 72.4% (95% CI, 67.0%-77.8%) and mean κ being 48.7% (95% CI, 37.3%-60.1%). The EEG-wise IRR was substantial, with the mean PA being 80.9% (95% CI, 76.2%-85.7%) and mean κ being 69.4% (95% CI, 60.3%-78.5%). A statistical model based on waveform morphological features, when provided with individualized thresholds, explained the median binary scores of all experts with a high degree of accuracy of 80% (range, 73%-88%).

Conclusions and Relevance

This study's findings suggest that experts can identify whether EEGs contain IEDs with substantial reliability. Lower reliability regarding individual IEDs may be largely explained by various experts applying different thresholds to a common underlying statistical model.

Nine-year prospective efficacy and safety of brain-responsive neurostimulation for focal epilepsy

OBJECTIVE

To prospectively evaluate safety and efficacy of brain-responsive neurostimulation in adults with medically intractable focal onset seizures (FOS) over 9 years.

METHODS

Adults treated with brain-responsive neurostimulation in 2-year feasibility or randomized controlled trials were enrolled in a long-term prospective open label trial (LTT) to assess safety, efficacy, and quality of life (QOL) over an additional 7 years. Safety was assessed as adverse events (AEs), efficacy as median percent change in seizure frequency and responder rate, and QOL with the Quality of Life in Epilepsy (QOLIE-89) inventory.

RESULTS

Of 256 patients treated in the initial trials, 230 participated in the LTT. At 9 years, the median percent reduction in seizure frequency was 75% (p < 0.0001, Wilcoxon signed rank), responder rate was 73%, and 35% had a ≥90% reduction in seizure frequency. We found that 18.4% (47 of 256) experienced ≥1 year of seizure freedom, with 62% (29 of 47) seizure-free at the last follow-up and an average seizure-free period of 3.2 years (range 1.04-9.6 years). Overall QOL and epilepsy-targeted and cognitive domains of QOLIE-89 remained significantly improved (p < 0.05). There were no serious AEs related to stimulation, and the sudden unexplained death in epilepsy (SUDEP) rate was significantly lower than predefined comparators (p < 0.05, 1-tailed χ2).

CONCLUSIONS

Adjunctive brain-responsive neurostimulation provides significant and sustained reductions in the frequency of FOS with improved QOL. Stimulation was well tolerated; implantation-related AEs were typical of other neurostimulation devices; and SUDEP rates were low.

CLINICALTRIALSGOV IDENTIFIER

NCT00572195.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that brain-responsive neurostimulation significantly reduces focal seizures with acceptable safety over 9 years.

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.

Seizure freedom as an outcome in epilepsy treatment clinical trials

Seizure freedom is recognized as the goal of epilepsy treatment by patients, families, and in treatment guidelines and is associated with notably improved quality of life. However, many studies of epilepsy treatments (including antiseizure medications/antiepileptic drugs, neurostimulation, and dietary therapies) fail to report data on seizure freedom. Even among studies that include this outcome, methods for defining and analyzing seizure freedom vary considerably. Thus, the available data are often difficult to interpret and comparisons between studies are particularly challenging. Although these issues had been identified over a decade ago, there remains a lack of clarity and standardized methods used in analyzing and reporting seizure freedom outcomes in studies of epilepsy treatments. In addition, it remains unclear whether short-term seizure freedom outcomes from pivotal clinical trials are predictive of longer-term seizure freedom outcomes for patients with treatment-refractory epilepsy. Ultimately, the limitations of the available data lead to the potential for misinterpretation and misunderstanding of seizure freedom outcomes associated with the spectrum of available treatments when examining treatment options for patients. Clearly defined outcome analyses of seizure freedom attainment and duration are essential in future clinical studies of treatment for seizures to guide treatment selection and modification for patients.

Cenobamate (YKP3089) as adjunctive treatment for uncontrolled focal seizures in a large, phase 3, multicenter, open-label safety study

Objective

During the development of cenobamate, an antiseizure medication (ASM) for focal seizures, three cases of drug reaction with eosinophilia and systemic symptoms (DRESS) occurred. To mitigate the rate of DRESS, a start‐low, go‐slow approach was studied in an ongoing, open‐label, multicenter study. Also examined were long‐term safety of cenobamate and a method for managing the pharmacokinetic interaction between cenobamate, a 2C19 inhibitor, and concomitant phenytoin or phenobarbital.

Methods

Patients 18‐70 years old with uncontrolled focal seizures taking stable doses of one to three ASMs were enrolled. Cenobamate 12.5 mg/d was initiated and increased at 2‐week intervals to 25, 50, 100, 150, and 200 mg/d. Additional biweekly 50 mg/d increases to 400 mg/d were allowed. During titration, patients taking phenytoin or phenobarbital could not have their cenobamate titration rate or other concomitant ASMs adjusted; phenytoin/phenobarbital doses could be decreased by 25%‐33%.

Results

At data cutoff (median treatment duration = 9 months), 1347 patients were enrolled, of whom 269 (20.0%) discontinued, most commonly due to adverse events (n = 137) and consent withdrawn for reason other than adverse event (n = 74); 1339 patients received ≥1 treatment dose (median modal dose = 200 mg). The most common treatment‐emergent adverse events (TEAEs) were somnolence (28.1%), dizziness (23.6%), and fatigue (16.6%). Serious TEAEs occurred in 108 patients (8.1%), most commonly seizure (n = 14), epilepsy (n = 5), and pneumonia, fall, and dizziness (n = 4 each). No cases of DRESS were identified. In the phenytoin/phenobarbital groups, 43.4% (36/114) and 29.7% (11/51) of patients, respectively, had their doses decreased. At the end of titration, mean plasma phenytoin/phenobarbital levels were generally comparable to baseline.

Significance

No cases of DRESS were identified in 1339 patients exposed to cenobamate using a start‐low (12.5 mg/d), go‐slow titration approach. Cenobamate was generally well tolerated in the long term, with no new safety issues found. Phenytoin/phenobarbital dose reductions (25%‐33%), when needed during cenobamate titration, maintained stable plasma levels.

Randomized trial of lacosamide versus fosphenytoin for nonconvulsive seizures

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.

Cannabidiol in patients with Lennox-Gastaut syndrome: Interim analysis of an open-label extension study

Objective

Patients with Lennox‐Gastaut syndrome (LGS) who completed 1 of 2 randomized, double‐blind, placebo‐controlled trials of add‐on cannabidiol (CBD) (GWPCARE3, NCT02224560 or GWPCARE4, NCT02224690) were invited to enroll in an open‐label extension (OLE) study evaluating the long‐term safety and efficacy of CBD (GWPCARE5, NCT02224573). Herein we present an interim analysis of the safety, efficacy, and patient‐reported outcomes from this trial.

Methods

Patients received a pharmaceutical formulation of highly purified CBD oral solution (Epidiolex; 100 mg/mL), titrated from 2.5 to 20 mg/kg/d over a 2‐week titration period, in addition to their existing medications. Doses could be reduced if not tolerated or increased up to 30 mg/kg/d if thought to be of benefit.

Results

This interim analysis was based on a November 2016 data cut. Of 368 patients who completed treatment in GWPCARE3 and GWPCARE4, 366 (99.5%) enrolled in the OLE study (GWPCARE5). Median treatment duration was 38 weeks at a mean modal dose of 23 mg/kg/d. Most patients (92.1%) experienced adverse events (AEs), primarily of mild (32.5%) or moderate (43.4%) severity. The most common AEs were diarrhea (26.8%), somnolence (23.5%), and convulsion (21.3%). Thirty‐five patients (9.6%) discontinued treatment due to AEs. Liver transaminase elevations were reported in 37 patients (10.1%), of whom 29 were receiving concomitant valproic acid; 34 cases resolved spontaneously or with dose modification of CBD or concomitant medication. Median reduction from baseline in drop seizure frequency (quantified monthly over 12‐week periods) ranged from 48% to 60% through week 48. Median reduction in monthly total seizure frequency ranged from 48% to 57% across all 12‐week periods through week 48. Eighty‐eight percent of patients/caregivers reported an improvement in the patient's overall condition per the Subject/Caregiver Global Impression of Change scale.

Significance

In this study, long‐term add‐on CBD treatment had an acceptable safety profile in patients with LGS and led to sustained reductions in seizures.

Interictal Epileptiform Discharge Detection in EEG in Different Practice Settings

OBJECTIVE

The goal of the study was to measure the performance of academic and private practice (PP) neurologists in detecting interictal epileptiform discharges in routine scalp EEG recordings.

METHODS

Thirty-five EEG scorers (EEGers) participated (19 academic and 16 PP) and marked the location of ETs in 200 30-second EEG segments using a web-based EEG annotation system. All participants provided board certification status, years of Epilepsy Fellowship Training (EFT), and years in practice. The Persyst P13 automated IED detection algorithm was also run on the EEG segments for comparison.

RESULTS

Academic EEGers had an average of 1.66 years of EFT versus 0.50 years of EFT for PP EEGers (P < 0.0001) and had higher rates of board certification. Inter-rater agreement for the 35 EEGers was fair. There was higher performance for EEGers in academics, with at least 1.5 years of EFT, and with American Board of Clinical Neurophysiology and American Board of Psychiatry and Neurology-E specialty board certification. The Persyst P13 algorithm at its default setting (perception value = 0.4) did not perform as well at the EEGers, but at substantially higher perception value settings, the algorithm performed almost as well human experts.

CONCLUSIONS

Inter-rater agreement among EEGers in both academic and PP settings varies considerably. Practice location, years of EFT, and board certification are associated with significantly higher performance for IED detection in routine scalp EEG. Continued medical education of PP neurologists and neurologists without EFT is needed to improve routine scalp EEG interpretation skills. The performance of automated detection algorithms is approaching that of human experts.

Non-invasive Seizure Localization with Ictal Single-Photon Emission Computed Tomography is Impacted by Preictal/Early Ictal Network Dynamics

OBJECTIVE

More than one third of children with epilepsy have medically intractable seizures. Promising therapies, including targeted neurostimulation and surgery, depend on accurate localization of the epileptogenic zone. Ictal perfusion Single-Photon Emission Computed Tomography (SPECT) can localize the seizure focus noninvasively, with comparable accuracy to that of invasive EEG. However, multiple factors including seizure dynamics may affect its spatial specificity.

METHODS

Using subtracted ictal from interictal SPECT and scalp EEG from 118 pediatric epilepsy patients (40 of whom had surgery after the SPECT studies), information theoretic measures of association and advanced statistical models, this study investigated the impact of preictal and ictal brain network dynamics on SPECT focality.

RESULTS

Network dynamics significantly impacted the SPECT localization ~30 s before to ~45 s following ictal onset. Distributed early ictal connectivity changes, indicative of a rapidly evolving seizure, were negatively associated with SPECT focality. Spatially localized connectivity changes later in the seizure, indicating slower seizure propagation, were positively associated with SPECT focality. In the first ~60 s of the seizure, significantly higher network connectivity was estimated in an area overlapping with the area of hyperperfusion. Finally, ~75% of patients with Engel class 1a/1b outcomes had SPECTs that were concordant with the resected area.

CONCLUSION

Slowly evolving seizures are more likely to be accurately imaged with SPECT, and the identified focus may overlap with brain regions where significant topological changes occur.

SIGNIFICANCE

Measures of preictal/early ictal network dynamics may help optimize the SPECT localization, leading to improved surgical and neurostimulation outcomes in refractory epilepsy.

American Clinical Neurophysiology Society: EEG Guidelines Introduction

This revision to the EEG Guidelines is an update incorporating current EEG technology and practice. "Standards of practice in clinical electroencephalography" (previously Guideline 4) has been removed. It is currently undergoing revision through collaboration among multiple medical societies and will become part of "Qualifications and Responsibilities of Personnel Performing and Interpreting Clinical Neurophysiology Procedures." The remaining guidelines are reordered and renumbered.

Detection of generalized tonic-clonic seizures using surface electromyographic monitoring

Objective

A prospective multicenter phase III trial was undertaken to evaluate the performance and tolerability in the epilepsy monitoring unit (EMU) of an investigational wearable surface electromyographic (sEMG) monitoring system for the detection of generalized tonic–clonic seizures (GTCSs).

Methods

One hundred ninety‐nine patients with a history of GTCSs who were admitted to the EMU in 11 level IV epilepsy centers for clinically indicated video‐electroencephalographic monitoring also received sEMG monitoring with a wearable device that was worn on the arm over the biceps muscle. All recorded sEMG data were processed at a central site using a previously developed detection algorithm. Detected GTCSs were compared to events verified by a majority of three expert reviewers.

Results

For all subjects, the detection algorithm detected 35 of 46 (76%, 95% confidence interval [CI] = 0.61–0.87) of the GTCSs, with a positive predictive value (PPV) of 0.03 and a mean false alarm rate (FAR) of 2.52 per 24 h. For data recorded while the device was placed over the midline of the biceps muscle, the system detected 29 of 29 GTCSs (100%, 95% CI = 0.88–1.00), with a detection delay averaging 7.70 s, a PPV of 6.2%, and a mean FAR of 1.44 per 24 h. Mild to moderate adverse events were reported in 28% (55 of 199) of subjects and led to study withdrawal in 9% (17 of 199). These adverse events consisted mostly of skin irritation caused by the electrode patch that resolved without treatment. No serious adverse events were reported.

Significance

Detection of GTCSs using an sEMG monitoring device on the biceps is feasible. Proper positioning of this device is important for accuracy, and for some patients, minimizing the number of false positives may be challenging.

Interictal epileptiform discharge characteristics underlying expert interrater agreement

OBJECTIVE

The presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED.

METHODS

IED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization.

RESULTS

Multiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11.

CONCLUSIONS

The features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer's scores perform best with a large group of EEGers (more than 10).

SIGNIFICANCE

By examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a "wisdom of the crowd" effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG.

The promise of subtraction ictal SPECT co-registered to MRI for improved seizure localization in pediatric epilepsies: Affecting factors and relationship to the surgical outcome

OBJECTIVE

Ictal SPECT is promising for accurate non-invasive localization of the epileptogenic brain tissue in focal epilepsies. However, high quality ictal scans require meticulous attention to the seizure onset. In a relatively large cohort of pediatric patients, this study investigated the impact of the timing of radiotracer injection, MRI findings and seizure characteristics on ictal SPECT localizations, and the relationship between concordance of ictal SPECT, scalp EEG and resected area with seizure freedom following epilepsy surgery.

METHODS

Scalp EEG and ictal SPECT studies from 95 patients (48 males and 47 females, median age=11years, (25th, 75th) quartiles=(6.0, 14.75) years) with pharmacoresistant focal epilepsy and no prior epilepsy surgery were reviewed. The ictal SPECT result was examined as a function of the radiotracer injection delay, seizure duration, epilepsy etiology, cerebral lobe of seizure onset identified by EEG and MRI findings. Thirty two patients who later underwent epilepsy surgery had postoperative seizure freedom data at <1, 6 and 12 months.

RESULTS

Sixty patients (63.2%) had positive SPECT localizations - 51 with a hyperperfused region that was concordant with the cerebral lobe of seizure origin identified by EEG and 9 with discordant localizations. Of these, 35 patients (58.3%) had temporal and 25 (41.7%) had extratemporal seizures. The ictal SPECT result was significantly correlated with the injection delay (p<0.01) and cerebral lobe of seizure onset (specifically frontal versus temporal; p=0.02) but not MRI findings (p=0.33), epilepsy etiology (p≥0.27) or seizure duration (p=0.20). Concordance of SPECT, scalp EEG and resected area was significantly correlated with seizure freedom at 6 months after surgery (p=0.04).

SIGNIFICANCE

Ictal SPECT holds promise as a powerful source imaging tool for presurgical planning in pediatric epilepsies. To optimize the SPECT result the radiotracer injection delay should be minimized to≤25s, although the origin of seizure onset (specifically temporal versus frontal) also significantly impacts the localization.

Sensitivity of quantitative EEG for seizure identification in the intensive care unit

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.

American Clinical Neurophysiology Society Guideline 4: Recording Clinical EEG on Digital Media

Digital EEG recording systems are now widely available and relatively inexpensive. They offer multiple advantages over previous analog/paper systems, such as higher fidelity recording, signal postprocessing, automated detection, and efficient data storage. This document provides guidance for the creation of digital EEG recordings including (1) documentation of patient information, (2) notation of information during the recording, (3) digital signal acquisition parameters during the recording, (4) storage of digital information, and (5) display of digital EEG signals.

A study of morphology-based wavelet features and multiple-wavelet strategy for EEG signal classification: results and selected statistical analysis

Automatic detection and classification of Epileptiform transients is an open and important clinical issue. In this paper, we test 5 feature sets derived from a group of morphology-based wavelet features and compare the results with that of a Guler-suggested feature set. We also implement a multiple-mother-wavelet strategy and compare performance with the usual single-mother-wavelet strategy. The results indicate that both the derived features and the multiple-mother-wavelet strategy improved classifier performance, using a variety of performance measures. We assess the statistical significance of the performance improvement of the new feature sets/strategy. In most cases, the performance improvement is either significant or highly significant.

Oscillating Square Wave Transcranial Direct Current Stimulation (tDCS) Delivered During Slow Wave Sleep Does Not Improve Declarative Memory More Than Sham: A Randomized Sham Controlled Crossover Study

BACKGROUND

A 2006 trial in healthy medical students found that anodal slow oscillating tDCS delivered bi-frontally during slow wave sleep had an enhancing effect in declarative, but not procedural memory. Although there have been supporting animal studies, and similar findings in pathological groups, this study has not been replicated, or refuted, in the intervening years. We therefore tested these earlier results for replication using similar methods with the exception of current waveform (square in our study, nearly sinusoidal in the original).

OBJECTIVE/HYPOTHESIS

Our objective was to test the findings of a 2006 trial suggesting bi-frontal anodal tDCS during slow wave sleep enhances declarative memory.

METHODS

Twelve students (mean age 25, 9 women) free of medical problems underwent two testing conditions (active, sham) in a randomized counterbalanced fashion. Active stimulation consisted of oscillating square wave tDCS delivered during early Non-Rapid Eye Movement (NREM) sleep. The sham condition consisted of setting-up the tDCS device and electrodes, but not turning it on during sleep. tDCS was delivered bi-frontally with anodes placed at F3/F4, and cathodes placed at mastoids. Current density was 0.517 mA/cm(2), and oscillated between zero and maximal current at a frequency of 0.75 Hz. Stimulation occurred during five-five minute blocks with 1-min inter-block intervals (25 min total stimulation). The primary outcomes were both declarative memory consolidation measured by a paired word association test (PWA), and non-declarative memory, measured by a non-dominant finger-tapping test (FTT). We also recorded and analyzed sleep EEG.

RESULTS

There was no difference in the number of paired word associations remembered before compared to after sleep [(active = 3.1 ± 3.0 SD more associations) (sham = 3.8 ± 3.1 SD more associations)]. Finger tapping improved, (non-significantly) following active stimulation [(3.6 ± 2.7 SD correctly typed sequences) compared to sham stimulation (2.3 ± 2.2 SD correctly typed sequences)].

CONCLUSION

In this study, we failed to find improvements in declarative or performance memory and could not replicate an earlier study using nearly identical settings. Specifically we failed to find a beneficial effect on either overnight declarative or non-declarative memory consolidation via square-wave oscillating tDCS intervention applied bi-frontally during early NREM sleep. It is unclear if the morphology of the tDCS pulse is critical in any memory related improvements.

Intravenous carbamazepine as short-term replacement therapy for oral carbamazepine in adults with epilepsy: Pooled tolerability results from two open-label trials

OBJECTIVE

To report tolerability findings and maintenance of seizure control from a pooled analysis of phase I open-label trial OV-1015 (NCT01079351) and phase III study 13181A (NCT01128959).

METHODS

Patients receiving a stable oral dosage of carbamazepine were switched to an intravenous (IV) carbamazepine formulation solubilized in a cyclodextrin matrix (at a 70% dosage conversion) for either a 15- or a 30-min infusion every 6 h for up to 7 days and then switched back. A subset of patients who tolerated 15-min infusions also received 2- to 5-min (rapid) infusions. Assessments included physical and laboratory evaluations, electrocardiography (ECG) studies, as well as adverse event (AE) monitoring for tolerability. Convulsion/seizure AE terms and data from seizure diaries were used as proxies for the assessment of consistency of seizure control between formulations.

RESULTS

Of the 203 patients exposed to IV carbamazepine (30 min, n = 43; 15 min, n = 160), 113 received 149 rapid infusions. During infusion, the most commonly reported AEs (≥ 5%) were dizziness (19%), somnolence (6%), headache (6%), and blurred vision (5%). IV carbamazepine was not associated with clinically relevant cardiac AEs. The tolerability profile appeared similar between patients who received <1,600 mg/day (n = 174) and ≥ 1,600 mg/day (n = 29) carbamazepine. Cyclodextrin exposure was not associated with clinically relevant changes in AEs or renal biomarkers. Seizure control was maintained as patients transitioned between oral and IV carbamazepine.

SIGNIFICANCE

IV carbamazepine administered as multiple 30- or 15-min infusions every 6 h, and as a single rapid infusion, was well tolerated as a short-term replacement in adults with epilepsy receiving stable dosages of oral carbamazepine. Infusion site reactions, which were generally mild, were the only unique AEs identified; seizure control was generally unchanged when patients were switching between formulations.

Inter-rater agreement on identification of electrographic seizures and periodic discharges in ICU EEG recordings

OBJECTIVE

This study investigated inter-rater agreement (IRA) among EEG experts for the identification of electrographic seizures and periodic discharges (PDs) in continuous ICU EEG recordings.

METHODS

Eight board-certified EEG experts independently identified seizures and PDs in thirty 1-h EEG segments which were selected from ICU EEG recordings collected from three medical centers. IRA was compared between seizure and PD identifications, as well as among rater groups that have passed an ICU EEG Certification Test, developed by the Critical Care EEG Monitoring Research Consortium (CCEMRC).

RESULTS

Both kappa and event-based IRA statistics showed higher mean values in identification of seizures compared to PDs (k=0.58 vs. 0.38; p<0.001). The group of rater pairs who had both passed the ICU EEG Certification Test had a significantly higher mean IRA in comparison to rater pairs in which neither had passed the test.

CONCLUSIONS

IRA among experts is significantly higher for identification of electrographic seizures compared to PDs. Additional instruction, such as the training module and certification test developed by the CCEMRC, could enhance this IRA.

SIGNIFICANCE

This study demonstrates more disagreement in the labeling of PDs in comparison to seizures. This may be improved by education about standard EEG nomenclature.

Morphology-based wavelet features and multiple mother wavelet strategy for spike classification in EEG signals

New wavelet-derived features and strategies that can improve autonomous EEG classifier performance are presented. Various feature sets based on the morphological structure of wavelet subband coefficients are derived and evaluated. The performance of these new feature sets is superior to Guler's classic features in both sensitivity and specificity. In addition, the use of (scalp electrode) spatial information is also shown to improve EEG classification. Finally, a new strategy based upon concurrent use of several mother wavelets is shown to result in increased sensitivity and specificity. Various attempts at reducing feature vector dimension are shown. A non-parametric method, k-NNR, is implemented for classification and 10-fold cross-validation is used for assessment.

Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis

The routine scalp electroencephalogram (rsEEG) is the most common clinical neurophysiology procedure. The most important role of rsEEG is to detect evidence of epilepsy, in the form of epileptiform transients (ETs), also known as spike or sharp wave discharges. Due to the wide variety of morphologies of ETs and their similarity to artifacts and waves that are part of the normal background activity, the task of ET detection is difficult and mistakes are frequently made. The development of reliable computerized detection of ETs in the EEG could assist physicians in interpreting rsEEGs. We report progress in developing a standardized database for testing and training ET detection algorithms. We describe a new version of our EEGnet software system for collecting expert opinion on EEG datasets, a completely web-browser based system. We report results of EEG scoring from a group of 11 board-certified academic clinical neurophysiologists who annotated 30-s excepts from rsEEG recordings from 100 different patients. The scorers had moderate inter-scorer reliability and low to moderate intra-scorer reliability. In order to measure the optimal size of this standardized rsEEG database, we used machine learning models to classify paroxysmal EEG activity in our database into ET and non-ET classes. Based on our results, it appears that our database will need to be larger than its current size. Also, our non-parametric classifier, an artificial neural network, performed better than our parametric Bayesian classifier. Of our feature sets, the wavelet feature set proved most useful for classification.

Hippocampal atrophy in temporal lobe epilepsy: the 'generator' and 'receiver'

OBJECTIVE

Some patients with unilateral medial temporal lobe epilepsy (MTLE) display bilateral hippocampal atrophy on MRI, even though seizures originate in only one hippocampus. The correct identification of the epileptogenic hippocampus (the 'generator') vs the non-epileptogenic (the 'receiver') may lead to better surgical planning and results.

MATERIALS AND METHODS

We studied 14 patients with MTLE (eight left and six right) who became seizure free after unilateral hippocampal resection, with hippocampal sclerosis confirmed by histology. Hippocampal tridimensional morphometry was performed comparing patients and healthy controls employing a voxel-wise Wilcoxon test. Results were corrected for multiple comparisons with the application of a False Discovery Rate (FDR)-corrected threshold for q < 0.05.

RESULTS

Patients with MTLE showed atrophy involving the ipsilateral hippocampus and the contralateral hippocampus, more pronouncedly within the ipsilateral hippocampus in the anterior-inferior aspect of the hippocampal head (left MTLE, left hippocampus x = -28, y = -16, z = -24, Z = 3.6; right MTLE, right hippocampus x = 22, y = -11, z = -27, Z = 2.9). On the contralateral hippocampus, the atrophy was more noticeable in the posterior head and body areas.

CONCLUSION

The epileptogenic hippocampal atrophy has an anatomically distinct pattern compared with the contralateral hippocampus. This information may help guide the presurgical assessment of MTLE.

Clinical perspectives on lacosamide

Despite the advent of new antiepileptic drugs (AEDs) over the past 15 years, the treatment of uncontrolled partial-onset seizures remains a dilemma for clinicians. The most recent AEDs offer new mechanisms of action and more favorable safety profiles than the first generation of AEDs. Lacosamide (LCM) is the latest AED awaiting approval by the FDA for adjunctive use in partial-onset seizures. It differs from all other approved AEDs in that it has two novel mechanisms of action and favorable pharmacokinetic and safety profiles. The purposes of this article are to present the significant parameters for its use in clinical practice, by summarizing the preliminary results of phase II and III clinical trials, and to compare its efficacy data with other second-generation AEDs.

Assessment of a scalp EEG-based automated seizure detection system

OBJECTIVE

The purpose of this study was to evaluate and validate an offline, automated scalp EEG-based seizure detection system and to compare its performance to commercially available seizure detection software.

METHODS

The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ∼3653h with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (∼1200h with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persyst's Reveal®, version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms.

RESULTS

The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24h, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24h, respectively. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p<0.05) smaller FDR.

CONCLUSIONS

The study validates the performance of the IdentEvent™ seizure detection system.

SIGNIFICANCE

With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.

Extrahippocampal gray matter loss and hippocampal deafferentation in patients with temporal lobe epilepsy

PURPOSE

Medial temporal epilepsy (MTLE) is associated with extrahippocampal brain atrophy. The mechanisms underlying brain damage in MTLE are unknown. Seizures may lead to neuronal damage, but another possible explanation is deafferentation from loss of hippocampal connections. This study aimed to investigate the relationship between hippocampal deafferentation and brain atrophy in MTLE.

METHODS

Three different MRI studies were performed involving 23 patients with unilateral MTLE (8 left and 15 right) and 34 healthy controls: (1) voxel-based morphometry (VBM), (2) diffusion tensor imaging (DTI) and (3) probabilistic tractography (PT). VBM was employed to define differences in regional gray matter volume (GMV) between controls and patients. Voxel-wise analyses of DTI evaluated differences in fractional anisotropy (FA), mean diffusivity (MD) and hippocampal PT. Z-scores were computed for regions-of-interest (ROI) GMV and peri-hippocampal FA and MD (to quantify hippocampal fiber integrity). The relationship between hippocampal deafferentation and regional GMV was investigated through the association between ROI Z scores and hippocampal fiber integrity.

RESULTS

Patients with MTLE exhibited a significant reduction in GMV and FA in perihippocampal and limbic areas. There was a decrease in hippocampal PT in patients with MTLE in limbic areas. A significant relationship between loss of hippocampal connections and regional GMV atrophy was found involving the putamen, pallidum, middle and inferior temporal areas, amygdala and ceberellar hemisphere.

DISCUSSION

There is a relationship between hippocampal disconnection and regional brain atrophy in MTLE. These results indicate that hippocampal deafferentation plays a contributory role in extrahippocampal brain damage in MTLE.

Seizure Detection Software Used to Complement the Visual Screening Process for Long-Term EEG Monitoring

It is widely recognized that visual screening of long-term EEG recordings can be time-consuming and labor-intensive due to the large volume of patient data produced daily in most Epilepsy Monitoring Units (EMUs). As a result, seizures, especially those with only electrographic changes, are sometimes overlooked, which for some patients could result in missed information for diagnosis, an unnecessarily prolonged hospital stay, and unavailable EMU beds for others. In this report, we propose that a better solution for identifying seizures in long-term EEG recording is to combine detection results from a reliable (high sensitivity and low false detection rate) automated detection system with EEG technologists' visual screening process. Using commercially available detection software, we present case studies that demonstrate potential benefits of this method that could help improve detection rates and bring greater efficiency to the seizure identification process in long-term EEG monitoring.