A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings

The purpose of this study was to evaluate a signal regularity-based automated seizure prediction algorithm for scalp EEG. Signal regularity was quantified using the Pattern Match Regularity Statistic (PMRS), a statistical measure. The primary feature of the prediction algorithm is the degree of convergence in PMRS (“PMRS entrainment”) among the electrode groups determined in the algorithm training process. The hypothesis is that the PMRS entrainment increases during the transition between interictal and ictal states, and therefore may serve as an indicator for prediction of an impending seizure.Запропоновано алгоритм автоматизованого прогнозування епілептичного нападу на основі аналізу регулярності сигналу ЕЕГ головного мозку. Регулярність сигналу розраховується на основі введеної величини регулярної статистики збігу фрагментів (Pattern Match Regularity Statistics — PMRS). Відмінною рисою алгоритму є ступінь збіжності в значеннях PMRS, розрахованих на основі показань із різних груп електродів, визначених у процесі навчання алгоритму. В основі алгоритму лежить гіпотеза про те, що збіжність у значеннях величини PMRS збільшується під час переходу в стан нападу і в такий спосіб може слугувати індикатором для прогнозування нападу

Signal regularity-based automated seizure detection system for scalp EEG monitoring

Розглянуто роботу автоматизованої системи реєстрації ЕЕГ головного мозку для раннього виявлення епілептичних нападів. Розроблено комп’ютерний алгоритм для перетворення складних багатоканальних сигналів ЕЕГ мозку на кілька динамічних показників, супроводжуваних дослідженнями їхніх просторово-часових властивостей. Робота алгоритму аналізується на великому клінічному наборі даних.The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multi-channel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset

Generalized periodic discharges and 'triphasic waves': A blinded evaluation of inter-rater agreement and clinical significance

Objectives: Generalized periodic discharges (GPDs) are associated with nonconvulsive seizures. Triphasic waves (TWs), a subtype of GPDs, have been described in relation to metabolic encephalopathy and not felt to be associated with seizures. We sought to establish the consistency of use of this descriptive term and its association with seizures. Methods: 11 experts in continuous EEG monitoring scored 20 cEEG samples containing GPDs using Standardized Critical Care EEG Terminology. In the absence of patient information, the inter-rater agreement (IRA) for EEG descriptors including TWs was assessed along with raters' clinical EEG interpretation and compared with actual patient information. Results: The IRA for 'generalized' and 'periodic' was near-perfect (kappa = 0.81), but fair for 'triphasic' (kappa = 0.33). Patients with TWs were as likely to develop seizures as those without (25% vs 26%, N.S.) and surprisingly, patients with TWs were less likely to have toxic-metabolic encephalopathy than those without TWs (55% vs 79%, p < 0.01). Conclusions: While IRA for the terms "generalized" and "periodic" is high, it is only fair for TWs. EEG interpreted as TWs presents similar risk for seizures as GPDs without triphasic appearance. GPDs are commonly associated with metabolic encephalopathy, but 'triphasic' appearance is not predictive. Significance: Conventional association of 'triphasic waves' with specific clinical conditions may lead to inaccurate EEG interpretation.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

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 whocompletedGWPCARE1 PartA(NCT02091206)orPartB,orGWPCARE2,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: Patientsreceived 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)occurredin97%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. Sixtynine (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 reductionsin seizure frequency in patients with treatment-resistant DS