Epilepsy Monitoring Unit Research Articles

Validity testing in veterans with epileptic seizures and psychogenic nonepileptic seizures

Identification of clinically meaningful subgroups among patients with psychogenic nonepileptic seizures (PNES) or epileptic seizures (ES) is of potential value for assessing prognosis and predicting therapeutic response. Invalid performance on validity tests has been associated with noncredible complaints and worse cognitive test scores, and may be one such classification criteria. We studied invalid performance in Veterans with PNES or ES, and the association of invalid performance with cognitive test scores and subjective complaints. Patients were consecutive admissions to three veterans affairs (VA) epilepsy monitoring units. Evaluations included two validity tests: the Test of Memory Malingering (TOMM); and the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF) over-reporting validity scales. We compared the frequency of invalid performance on the TOMM or MMPI-2-RF in patients diagnosed with PNES vs. ES. We evaluated the association of invalid performance with scores on the Repeatable Battery for Assessment of Neuropsychological Status (RBANS), and four subjective symptom measures including the Beck Depression Inventory-II, and Quality of Life in Epilepsy-31. Invalid TOMM performance was found in 25.3% of Veterans diagnosed with PNES and 10.8% of those with ES (p = .03). Invalid reporting on the MMPI-2-RF was found in 35.9% of the PNES group vs. 15.3% of the ES group (p = .01). Effects of valid vs. invalid reporting on external measures were similar for ES and PNES groups. Patients with invalid vs. valid TOMM performance had lower scores on the RBANS (p < .001). Patients with invalid performance had greater complaints on all subjective measures, with largest effect sizes for the MMPI-2-RF validity scales (p < .001). In Veterans admitted for evaluation of poorly controlled seizures, invalid performance on validity tests was not uncommon. Cognitive test results and subjective reports from patients with invalid performance may not be credible. These observations have implications for the analysis of clinical trials, where primary and secondary outcomes often rely on self-report measures.

Comparative study of Pre and Post-operative Drug Costs in Patients with Refractory Epilepsy Undergoing Hemispherectomy and Temporal Lobectomy at the National Children’s Hospital Dr. Carlos Sáenz Herrera

Introduction: Refractory epilepsy can be defined as a failure of adequate trials of antiepileptic drug programs tolerated and appropriately chosen and used (either as monotherapy or in combination) to achieve sustained freedom from seizures. Surgical procedures for the control of epileptic seizures have spread throughout the world, however, large medical centers have mainly focused on studying and analyzing the clinical results of their surgical interventions. For this reason, this study aims to analyze the cost of pre and post-surgical anticonvulsant medications in patients. Objective: To analyze the relationship between preoperative cost / postoperative cost of antiepileptic drugs in patients undergoing hemispherectomy or temporary lobectomy at the National Children’s Hospital (Hospital Nacional de Niños-HNN). Materials and methods: This study is a retrospective, descriptive, quantitative and longitudinal type of a series of patients who were operated in the Epilepsy Monitoring and Surgery Unit (UMCE) of Costa Rica, between November 2000 and December 2018. Information on the history of medications is obtained from patients before and after having undergone hemispherectomy or temporal lobectomy to assess whether or not there is an economic saving in this aspect when comparing both periods of time. Results: 69 patients have undergone hemispherectomy and temporal lobectomy since the creation of the UMCE in November 2000. Of these, 18 (26%) underwent hemispherectomy and 51 (74%) underwent temporary lobectomy. A sample of 31 patients (45%) were obtained, which met the inclusion criteria. The average age of initiation of treatment at the HNN was 5.5 years old (66 months) and the average age at which they underwent surgery was 10.8 years old (130 months). There was one patient in the sample who received 9 anticonvulsant medications before undergoing the surgical procedure and the average number of medications used pre-surgically was 4.45 anticovulsivants per patient. The average postoperative medication was 2.35 anticonvulsivants per patient. The average monthly preoperative cost was $50.17, while the postoperative period was $31.26, with a difference of $18.91. A total of 9 patients in the sample increased the monthly postoperative cost. The total savings per month in the patients included in the sample was $586.36 and extrapolated to the population of 69 patients resulting in a saving of $1,305.14 per month. The projection according to the life expectancy of the patients in the sample translates into a saving of $409,188.59 while the projection to the 69 patients would result in a saving of $910,774.60 for the Caja Costarricense del Seguro Social (CCSS - Costa Rican Social Security Fund). Discussion: The effectiveness of these two surgeries in suitably selected patients is reflected in the economic savings represented by antiepileptic drugs administered after these surgical procedures. Conclusion: we consider that in this center the patients who have been operated have meant a saving for the Caja Costarricense del Seguro Social (CCSS) since they have diminished or completely eliminated the anti-epileptic medical treatment

The semantics of epileptic and psychogenic nonepileptic seizures and their differential diagnosis

AimsThe aim of the study was to highlight the fundamental linguistic elements through a simplified scoring table to build a simplified and easier diagnostic model for linguistic evaluation by nonexperts. MethodsThe study was based on interviews performed in patients recruited at the University of Milano Bicocca, at the Epilepsy Center of the University of Messina, and at the Epilepsy Monitoring Unit of the University of Rochester, who experienced “seizures” and underwent video-electroencephalogram (vEEG) for differential diagnosis. All enrolled subjects underwent a video-recorded interview consisting of a fixed sequence of five questions. Subsequently, a researcher examined the video recordings, blind to the vEEG results, and filled in the simplified linguistic evaluation (SLE) scoring table. The best cutoff score for the diagnosis was determined using a ROC curve and was selected as the value leading to the maximum number of patients correctly classified. ResultsThe study sample consisted of 35 interviews.The receiver operating characteristics (ROC) curve analysis showed that the best cutoff for the diagnosis was 12. The accuracy was 82.9% for this cutoff value. The area under the ROC curve (AUC) was 0.81 (95% confidence interval (CI): 0.66–0.97). Classifying patients using the SLE scoring table as diagnostic tool, with the selected cutoff score, 17 out of 19 patients with psychogenic nonepileptic seizures (PNES) were correctly classified (89.5% sensitivity), while 12 out of 16 patients with epileptic seizures (ES) were correctly classified (75% specificity). Positive and negative predictive values are, respectively, 81% and 85.7%. ConclusionsDespite some limitations, the use of the SLE scoring table may reduce costs, and conversation analysis (CA) might help achieving a timely and reliable diagnosis of PNES.

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an important tool in the diagnosis of brain disorders. The robust automatic classification of EEG signals is an important step towards detecting a brain disorder in its earlier stages before status deterioration. Motivated by the computation capabilities of natural evolution strategies (NES), this paper introduces an effective automatic classification approach denoted as natural evolution optimization-based deep learning (NEODL). The proposed classifier is an ingredient in a signal processing chain that comprises other state-of-the-art techniques in a consistent framework for the purpose of automatic EEG classification. The proposed framework consists of four steps. First, the L1-principal component analysis technique is used to enhance the raw EEG signal against any expected artifacts or noise. Second, the purified EEG signal is decomposed into a number of sub-bands by applying the wavelet transform technique where a number of spectral and statistical features are extracted. Third, the extracted features are examined using the artificial bee colony approach in order to optimally select the best features. Lastly, the selected features are treated using the proposed NEODL classifier, where the input signal is classified according to the problem at hand. The proposed approach is evaluated using two benchmark datasets and addresses two neurological disorder applications: epilepsy disease and motor imagery. Several experiments are conducted where the proposed classifier outperforms other deep learning techniques as well as other existing approaches. The proposed framework, including the proposed classifier (NEODL), has a promising performance in the classification of EEG signals, including epilepsy disease and motor imagery. Based on the given results, it is expected that this approach will also be useful for the identification of the epileptogenic areas in the human brain. Accordingly, it may find application in the neuro-intensive care units, epilepsy monitoring units, and practical brain-computer interface systems in clinics.

The role of the epilepsy monitoring unit in the investigation of patients with epilepsy and intellectual disabilities

BackgroundA significant proportion of the people with intellectual disabilities (ID) has epilepsy and lives in institutions. These patients tend to have atypical presentations of epileptic seizures with an increased risk of misdiagnoses. They often have drug-resistant epilepsy (DRE) requiring polypharmacy with increased risk of morbidity. The aim of this study was to determine the usefulness of Epilepsy Monitoring Unit (EMU) in the diagnosis and management of these patients. MethodsThis is a retrospective observational study of people with epilepsy and ID living in institutions that were admitted to the EMU at London Health Sciences Center (LHSC), from January 2014 to December 2016. ResultsOut of 1121 patients admitted to the EMU at the LHSC, 1.96% (N = 22) fulfilled the inclusion criteria for this study. The mean age was 34.5 years (interquartile range [IQR]: 28.8–53); 50%(N = 11) were female. Fourteen (63.6%) had generalized epilepsy. Six (27.3%) had a history of status epilepticus. The mean number of antiseizure medications (ASMs) in those patients was three (IQR: 2–4). Eight (36.4%) patients had severely impaired or no language skills and seven (31.8%) required wheelchair. Eleven (50%) had a mood disorder and seven (31.8%) of them were taking antipsychotic medications. The mean duration of admission duration was 6.6 days (IQR: 3.5–8.5). There was a clinical-electrographic correlation between the behavioral events and epileptic seizures in nineteen (86.4%) of the patients. ConclusionsAdmission to the EMU provided an accurate characterization of transient events in people with ID and epilepsy with improvement in their medical management.

Utilization of MEG Among the US Epilepsy Centers: A Survey-Based Appraisal.

The purported underutilization of magnetoencephalography (MEG) among the USA epilepsy centers has never been studied, and any evidence-based understanding of its magnitude is lacking. Two hundred twenty-five National Association of Epilepsy Centers centers (2016) were invited to participate anonymously in a 13-question web-based survey of clinical practice focused on MEG use. On average, centers (N = 70; 61 of which were level 4) reported <6 epileptologists, >7 dedicated epilepsy monitoring unit beds, 206 phase 1 studies, 15 phase 2 studies, 10 direct resections, and 9 indirect resections; 27% owned MEG. On average, 11.2 MEGs per year were ordered for epilepsy localization and 7.6 for any presurgical mapping modalities. Wada test aka the intracarotid sodium amobarbital procedure (ISAP) (43%) and functional MRI (29%) were preferred over MEG (4%) for language mapping. The number of epileptologists and the number of epilepsy monitoring unit beds correlated positively with the most clinical volumes. The centers who own a MEG had surgical volumes significantly higher than those without. The number and complexity of patients as well as the proximity of a MEG were perceived as significant contributors/obstacles to increased MEG use. Only the centers with larger surgical volumes incorporate MEG regularly in presurgical evaluation of patients with drug-resistant epilepsy. A reversal of the pervasive underutilization of epilepsy surgery can benefit from MEG, but this requires a sustained concerted promotion by the epilepsy and MEG communities.

Using ICD-10 codes to identify elective epilepsy monitoring unit admissions from administrative billing data: A validation study

Video-electroencephalogram (EEG) monitoring in the epilepsy monitoring unit (EMU) is essential for managing epilepsy and seizure mimics. Evaluation of care in the EMU would benefit from a validated code set capable of identifying EMU admissions from administrative databases comprised of large, diverse cohorts. We assessed the ability of code-based queries to parse EMU admissions from administrative billing records in a large academic medical center over a four-year period, 2016–2019. We applied prespecified queries for admissions coded as follows: 1) elective, 2) receiving video-EEG monitoring, and 3) including diagnoses typically required by major US healthcare payers for EMU admission. Sensitivity (Sn), specificity (Sp), and predictive value positive/negative (PVP, PVN) were determined. Two approaches were highly effective. Incorporating epilepsy, seizure, or seizure mimic codes as the admitting diagnosis (assigned at admission; Sn 96.3%, Sp 100.0%, PVP 98.3%, and PVN 100.0%) or the principal diagnosis (assigned after discharge; Sn 94.9%, Sp 100.0%, PVP 98.8%, and PVN 100.0%) identified elective adult EMU admissions with comparable reliability (p = 0.096). The addition of surgical procedure codes further separated EMU admissions for intracranial EEG monitoring. When applied to larger, more comprehensive datasets, these code-based queries should enhance our understanding of EMU utilization and access to care on a scalable basis.

Using sampled visual EEG review in combination with automated detection software at the EMU

PurposeComplete visual review of prolonged video-EEG recordings at an EMU (Epilepsy Monitoring Unit) is time consuming and can cause problems in times of paucity of educated personnel. In this study we aimed to show non inferiority for electroclinical diagnosis using sampled review in combination with EEG analysis softreferware (P13 software, Persyst Corporation), in comparison to complete visual review. MethodFifty prolonged video-EEG recordings in adults were prospectively evaluated using sampled visual EEG review in combination with automated detection software of the complete EEG record. Visually assessed samples consisted of one hour during wakefulness, one hour during sleep, half an hour of wakefulness after wake-up and all clinical events marked by the individual and/or nurses. The final electro-clinical diagnosis of this new review approach was compared with the electro-clinical diagnosis after complete visual review as presently used. ResultsThe electro-clinical diagnosis based on sampled visual review combined with automated detection software did not differ from the diagnosis based on complete visual review. Furthermore, the detection software was able to detect all records containing epileptiform abnormalities and epileptic seizures. ConclusionSampled visual review in combination with automated detection using Persyst 13 is non-inferior to complete visual review for electroclinical diagnosis of prolonged video-EEG at an EMU setting, which makes this approach promising.

CTnI, BNP and CRP profiling after seizures in patients with drug-resistant epilepsy

PurposeTo profile serum levels of high sensitivity Troponin I (hs-cTnI), B-Type Natriuretic Peptide (BNP), and high sensitivity C Reactive Protein (hs-CRP), after epileptic seizures in patients with focal drug-resistant epilepsy, relating the results to the revised SUDEP-7 inventory. MethodsWe prospectively evaluated patients admitted to our Epilepsy Monitoring Unit. hs-cTnI, BNP, and hs-CRP were measured at admission and after the first seizure. The revised SUDEP-7 Risk Inventory was calculated. The statistical significance level was set at 0.05. ResultsFifty-eight patients were included (53.4 % female). The index seizure was a focal to bilateral tonic-clonic seizure (FBTCS) in 25.9 % of the patients, and 17.5 % had post-ictal generalized EEG suppression (PGES). After the seizure, 25.9 % had a significant (above 50 %) increase in hs-cTnI, 23.3 % in BNP, and 4.3 % in hs-CRP. About 40 % had cardiovascular risk factors (CRF), without known cardiac disease. The elevation of one biomarker did not compel the elevation of another. hs-cTnI increase was associated with FBTCS, PGES, longer seizures, maximal ictal heart rate, and HR change. Increases in BNP were associated with CRF. hs-CRP increase was associated with PGES. We found no significant association between SUDEP-7 and any biomarker increase. SignificanceSeveral patients had increases in biomarkers of myocardial necrosis/dysfunction after seizures, without significant association with the SUDEP-7 inventory. Different patterns of biomarkers’ elevations point to multifactorial pathophysiologies hypothetically associated with incipient myocardial lesions. A larger cohort with follow-up data could help to clarify the clinical relevance of these findings.

Pitfalls in Scalp High-Frequency Oscillation Detection From Long-Term EEG Monitoring.

Aims: Intracranially recorded high-frequency oscillations (>80 Hz) are considered a candidate epilepsy biomarker. Recent studies claimed their detectability on the scalp surface. We aimed to investigate the applicability of high-frequency oscillation analysis to routine surface EEG obtained at an epilepsy monitoring unit.Methods: We retrospectively analyzed surface EEGs of 18 patients with focal epilepsy and six controls, recorded during sleep under maximal medication withdrawal. As a proof of principle, the occurrence of motor task-related events during wakefulness was analyzed in a subsample of six patients with seizure- or syncope-related motor symptoms. Ripples (80–250 Hz) and fast ripples (>250 Hz) were identified by semi-automatic detection. Using semi-parametric statistics, differences in spontaneous and task-related occurrence rates were examined within subjects and between diagnostic groups considering the factors diagnosis, brain region, ripple type, and task condition.Results: We detected high-frequency oscillations in 17 out of 18 patients and in four out of six controls. Results did not show statistically significant differences in the mean rates of event occurrences, neither regarding the laterality of the epileptic focus, nor with respect to active and inactive task conditions, or the moving hand laterality. Significant differences in general spontaneous incidence [WTS(1) = 9.594; p = 0.005] that indicated higher rates of fast ripples compared to ripples, notably in patients with epilepsy compared to the control group, may be explained by variations in data quality.Conclusion: The current analysis methods are prone to biases. A common agreement on a standard operating procedure is needed to ensure reliable and economic detection of high-frequency oscillations.

Socioeconomic disparities in patients with psychogenic nonepileptic seizures and their caregivers

ObjectiveThere is no information on disparities of patients with psychogenic nonepileptic seizures (PNES) and their caregivers. The objective of this exploratory study is to compare patients with PNES and caregivers with low socioeconomic status (SES) with those of high SES for disparities in healthcare use, seizures, medication adverse effects, psychosocial impact, and knowledge about epilepsy. MethodsPatients with PNES and caregivers completed surveys about the aforementioned outcomes during their Epilepsy Monitoring Unit (EMU) admission. Associations were evaluated using SES as a binary independent variable and the patient- and caregiver-related outcomes as dependent variables. ResultsForty-three patients and 28 caregivers were recruited. The majority of patients were on average 36 years old, single women, unemployed, with some college education. The majority had PNES for 8 years averaging 20 seizures per month and were maintained on ≥2 antiepileptic drugs (AEDs) prior to their EMU admission. Most caregivers were first-degree relatives with a mean age of 43 years, married employed women of higher educational attainment, typically cohabitating with the patients. Low SES patients showed poorer knowledge about epilepsy (p < 0.0001) and higher anxiety levels (p = 0.03). Conversely, high SES patients demonstrated poorer social functioning (p = 0.04). High SES caregivers showed higher caregiving burden (p = 0.01). ConclusionThere are noteworthy disparities in patients with PNES of different SES and their caregivers. Identification of those disparities is a critical step in the creation of appropriate interventions to address them.

Post‐ictal accelerometer silence as a marker of post‐ictal immobility

Movement-based wearable sensors are used for detection of convulsive seizures. The identification of the absence of motion following a seizure, known as post-ictal immobility (PI), may represent a potential additional application of wearables. PI has been associated with potentially life-threatening complications and with sudden unexpected death in epilepsy (SUDEP). We aimed to assess whether wearable accelerometers (ACCs) could be used as a digital marker of PI. Devices with embedded ACCs were worn by patients admitted to an epilepsy monitoring unit. Participants presenting with convulsive seizures were included in the study. PI presence and duration were assessed by experts reviewing video recordings. An algorithm for the automatic detection of post-ictal ACC silence and its duration was developed and the linear pairwise relationship between the automatically detected duration of post-ictal ACC silence and the duration of the expert-labeled PI was analyzed. Twenty-two convulsive seizures were recorded from 18 study participants. Twenty were followed by PI and two by agitation. The automated estimation of post-ictal ACC silence identified all the 20 expert-labeled PI. The regression showed that the duration of the post-ictal ACC silence was correlated with the duration of PI (Pearson r=.92; P<.001), with the age of study participants (Pearson r=.78; P<.001), and with the duration of post-ictal generalized electroencephalography suppression (PGES; Pearson r=.4; P=.033). We highlight a novel application of wearables as a way to record post-ictal manifestations associated with an increased risk of SUDEP. The occurrence of a fatal seizure is unpredictable and the continuous, non-invasive, long-term identification of risk factors associated with each individual seizure may assume a great clinical importance.

Differentiation of Epileptic and Psychogenic Nonepileptic Seizures Using Single-Channel Surface Electromyography.

Epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) are difficult to differentiate when based on a patient's self-reported symptoms. This study proposes review of objective data captured by a surface electromyography (sEMG) wearable device for classification of events as ES or PNES. This may help clinicians accurately identify ES and PNES. Seventy-one subjects were prospectively enrolled across epilepsy monitoring units at VA Epilepsy Centers of Excellence. Subjects were concomitantly monitored using video EEG and a wearable sEMG epilepsy monitor, the Sensing Portable sEmg Analysis Characterization (SPEAC) System. Three epileptologists independently classified ES and PNES that contained upper extremity motor activity based on video EEG. The sEMG data from those events were automatically processed to provide a seizure score for event classification. After brief training (60 minutes), the sEMG data were reviewed by a separate group of four epileptologists to independently classify events as ES or PNES. According to video EEG review, 17 subjects experienced 34 events (15 ES and 19 PNES with upper extremity motor activity). The automated process correctly classified 87% of ES (positive predictive value = 88%, negative predictive value = 76%) and 79% of PNES, and the expert reviewers correctly classified 77% of ES (positive predictive value = 94%, negative predictive value = 84%) and 96% of PNES. The automated process and the expert reviewers correctly classified 100% of tonic-clonic seizures as ES, and 71 and 50%, respectively, of non-tonic-clonic ES. Automated and expert review, particularly in combination, of sEMG captured by a wearable seizure monitor (SPEAC System) may be able to differentiate ES (especially tonic-clonic) and PNES with upper extremity motor activity.

Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset.

Purpose:To compare the seizure detection performance of three expert humans and two computer algorithms in a large set of epilepsy monitoring unit EEG recordings.Methods:One hundred twenty prolonged EEGs, 100 containing clinically reported EEG-evident seizures, were evaluated. Seizures were marked by the experts and algorithms. Pairwise sensitivity and false-positive rates were calculated for each human–human and algorithm–human pair. Differences in human pairwise performance were calculated and compared with the range of algorithm versus human performance differences as a type of statistical modified Turing test.Results:A total of 411 individual seizure events were marked by the experts in 2,805 hours of EEG. Mean, pairwise human sensitivities and false-positive rates were 84.9%, 73.7%, and 72.5%, and 1.0, 0.4, and 1.0/day, respectively. Only the Persyst 14 algorithm was comparable with humans—78.2% and 1.0/day. Evaluation of pairwise differences in sensitivity and false-positive rate demonstrated that Persyst 14 met statistical noninferiority criteria compared with the expert humans.Conclusions:Evaluating typical prolonged EEG recordings, human experts had a modest level of agreement in seizure marking and low false-positive rates. The Persyst 14 algorithm was statistically noninferior to the humans. For the first time, a seizure detection algorithm and human experts performed similarly.

Postictal stertor: Associations with focal and bilateral seizure types

ObjectiveThe objective of the present study was to determine the association between respiratory stertor and focal and bilateral seizure types. MethodsWe characterized ictal and postictal behaviors during symmetric bilateral tonic–clonic (TC) and asymmetric TC seizures in the Johns Hopkins University (JHU) epilepsy monitoring unit, comparing these to focal unaware seizures. We measured the presence and duration of postictal stertorous respirations, postictal generalized electroencephalographic suppression (PGES), immobility/motor dysfunction, and encephalopathy and determined their associations and relationship to seizure types. ResultsIn initial seizures recorded in 80 consecutive patients, bilateral symmetric TC seizures (N = 35) were strongly associated with PGES (97%, p < 0.001) and postictal stertorous respirations (89%, p < 0.001). Only 10% of the 20 patients with asymmetric TC seizures had brief PGES; focal unaware seizures (N = 25) were not associated with PGES or stertorous breathing. Some patients (24%) with asymmetric or bilateral symmetric TC seizures had severe postictal encephalopathy with stertor that was separate or extended beyond periods of PGES. ConclusionBilateral symmetric TC seizures, but not focal unaware seizures, have postictal stertor during PGES. Severe postictal encephalopathy, however, is also associated with motor dysfunction and stertor. Stertor appears to be a compensatory postictal respiratory pattern for ictal/postictal hypoxemia and occurs with PGES or postictal encephalopathy.

Interaction of Temporal Lobe Epilepsy and Posttraumatic Stress Disorder: Network Analysis of a Single Case.

In this case study, we present a 21 years old female with long-standing Temporal Lobe Epilepsy (TLE) who, following a sexual assault, also developed Posttraumatic Stress Disorder (PTSD), leading to a change in her seizure semiology. The new seizures seemed to be a re-enactment of the sexual assault and accordingly were at first thought to be Psychogenic Non-Epileptic Seizures (PNES). Nevertheless, electroencephalography (EEG) recording at the Epilepsy Monitoring Unit (EMU) revealed ictal epileptic brain activity during these new attacks. In order to further explore the nature of the relation between epileptic seizures and PTSD symptomatology, a functional MRI (fMRI) scan was conducted focusing on neural response to threat (fearful faces). The results indicated that the response to threat elicited bilateral amygdala activation, as well as enhanced amygdala connectivity with the insula and anterior cingulate cortex (ACC), all central nodes of the fear circuitry. Accordingly, we suggest that this unique presentation of “pseudo” PNES might stem from the anatomical proximity of the epileptic network in this patient (temporal-insular-frontal) to the fear circuitry, allowing abnormal epileptic activity to “exploit” or activate the fear circuit or vice versa. We further propose that the traumatic experience may have changed the patient’s ictal semiology by modifying the course of the spread of the ictal activity toward the PTSD network.

Antiepileptic drug withdrawal and seizure severity in the epilepsy monitoring unit

ObjectiveThe goal of this study was to identify a strategy for antiepileptic drug (AED) reduction to allow efficient recording of focal seizures (FS) in patients undergoing video-electroencephalography (EEG) in an epilepsy monitoring unit (EMU) while avoiding the risk of complications associated with more severe seizure types. MethodsWe retrospectively reviewed consecutive patients admitted to our institution's EMU from July 1, 2016 to December 31, 2017. We included 114 presurgical patients who had AEDs reduced and at least one seizure during the admission. We compared AED dosages at which FS versus focal to bilateral tonic–clonic seizures (f-BTCS), seizure clusters, and lorazepam administration occurred. We also examined rate of AED reduction and seizure types. We used a receiver-operating characteristic (ROC) curve to identify a dose maximizing FS and minimizing other seizure types. ResultsAntiepileptic drug withdrawal rates ranged from 0 to 100% in the first 24 h (mean: 20%, standard deviation: 20%). Focal to bilateral tonic–clonic seizures and lorazepam administration occurred at a lower median AED dose than did FS (0%, 7.2%, and 43.8%, respectively, expressed as a percentage of the patient's outpatient daily AED dose; p < 0.001). A daily EMU-administered dose of one-third of the patient's outpatient AED dose allowed 55.0% of FS to occur while avoiding 82.0% of more severe seizure types. The seizure types had no difference in rate of AED withdrawal in the first 24 h of EMU stay. ConclusionsFocal seizures occurred at a higher AED dose than did f-BTCS. This may imply that a low minimally effective dose of AED could allow FS to be recorded while providing protection against f-BTCS. This strategy could improve efficacy and safety in the EMU.

Randomized open-label trial of intravenous brivaracetam versus lorazepam for acute treatment of increased seizure activity

ObjectiveThe objective of the present trial was to assess efficacy and safety of intravenous (IV) brivaracetam (BRV) vs. lorazepam (LZP) in patients with epilepsy undergoing evaluation in an epilepsy monitoring unit (EMU) who experienced seizures requiring acute treatment. MethodsThis was a phase 2, open-label, randomized, active-control, proof-of-concept trial (EP0087; NCT03021018). Patients (18–70 years) admitted to EMU were randomized 1:1:1 to single-dose bolus IV LZP (dose per investigator's practice), IV BRV 100 mg, or IV BRV 200 mg. Trial medication had to be administered within 30 min of qualifying seizure. Primary efficacy outcome was time to next seizure (clinical observation with electroencephalogram [EEG] confirmation) or to rescue medication use within 12 h of trial medication administration. Secondary outcomes included seizure freedom and rescue medication use within 12 h of trial medication administration. Safety and tolerability outcomes included treatment-emergent adverse events (TEAEs). ResultsOverall, 46 patients were randomized, and 45 received trial medication for a qualifying seizure. Patients in the LZP arm had doses from 1 to 4 mg (median: 1 mg). Eleven of 45 patients had a seizure within 12 h of trial medication administration (LZP 5/15 [median time to next seizure: 5.55 h], BRV 100 mg 3/15 [5.97 h], BRV 200 mg 3/15 [3.60 h]). No patients received additional rescue medication to control their qualifying seizure. Most patients were seizure-free over 12 h (LZP 9/15 [60.0%], BRV 100 mg 12/15 [80.0%], BRV 200 mg 12/15 [80.0%]). Rescue medication use within 12 h was numerically higher for LZP (6/15 [40.0%]) vs. BRV 100 mg (1/15 [6.7%]) and vs. BRV 200 mg (2/15 [13.3%]). Treatment-emergent adverse events were reported by 5/16 (31.3%), 6/15 (40.0%), and 3/15 (20.0%) of LZP, BRV 100 mg, and BRV 200 mg patients; one LZP patient had a serious TEAE (seizure cluster). Most common TEAEs (≥10% of patients) were sedation and somnolence with LZP, and dizziness, headache, and nausea with BRV. SignificanceIntravenous LZP, IV BRV 100 mg, and IV BRV 200 mg showed similar efficacy in controlling acute seizure activity in the EMU. Treatment-emergent adverse events were as expected for each medication. Although this trial should be interpreted with caution because of small patient numbers, it suggests a possible role of BRV in the acute treatment of increased seizure activity.

Comparative Acute Impact of Focal and Generalized Tonic-clonic Seizures on Cardiac Electrical Instability in the Epilepsy Monitoring Unit (1493)

Comparative Acute Impact of Focal and Generalized Tonic-clonic Seizures on Cardiac Electrical Instability in the Epilepsy Monitoring Unit (1493)

Automated Detection Software Improves Seizure Recognition in an Epilepsy Monitoring Unit (234)

Automated Detection Software Improves Seizure Recognition in an Epilepsy Monitoring Unit (234)