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Objective: This study was undertaken to ascertain the natural history and patterns of antiseizure medication (ASM) use in newly diagnosed focal epilepsy patients who were initially started on monotherapy., Methods: The data were derived from the Human Epilepsy Project. Differences between the durations of the most commonly first prescribed ASM monotherapies were assessed using a Cox proportional hazards model. Subjects were classified into three groups: monotherapy, sequential monotherapy, and polytherapy., Results: A total of 443 patients were included in the analysis, with a median age of 32 years (interquartile range [IQR] = 20-44) and median follow-up time of 3.2 years (IQR = 2.4-4.2); 161 (36.3%) patients remained on monotherapy with their initially prescribed ASM at the time of their last follow-up. The mean (SEM) and median (IQR) duration that patients stayed on monotherapy with their initial ASM was 2.1 (2.0-2.2) and 1.9 (.3-3.5) years, respectively. The most commonly prescribed initial ASM was levetiracetam (254, 57.3%), followed by lamotrigine (77, 17.4%), oxcarbazepine (38, 8.6%), and carbamazepine (24, 5.4%). Among those who did not remain on the initial monotherapy, 167 (59.2%) transitioned to another ASM as monotherapy (sequential monotherapy) and 115 (40.8%) ended up on polytherapy. Patients remained significantly longer on lamotrigine (mean = 2.8 years, median = 3.1 years) compared to levetiracetam (mean = 2.0 years, median = 1.5 years) as a first prescribed medication (hazard ratio = 1.5, 95% confidence interval = 1.0-2.2). As the study progressed, the proportion of patients on lamotrigine, carbamazepine, and oxcarbazepine as well as other sodium channel agents increased from a little more than one third (154, 34.8%) of patients to more than two thirds (303, 68.4%) of patients., Significance: Slightly more than one third of focal epilepsy patients remain on monotherapy with their first prescribed ASM. Approximately three in five patients transition to monotherapy with another ASM, whereas approximately two in five end up on polytherapy. Patients remain on lamotrigine for a longer duration compared to levetiracetam when it is prescribed as the initial monotherapy., (© 2023 International League Against Epilepsy.)

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., (© 2023 American Academy of Neurology.)

Purpose of Review: Seizures, including status epilepticus, have been reported in association with acute COVID-19 infection. People with epilepsy (PWE) have suffered from seizure exacerbations during the pandemic. This article reviews the data for clinical and electrographic seizures associated with COVID-19, technical EEG considerations for reducing risk of transmission, and factors contributing to seizure exacerbations in PWE as well as strategies to address this issue., Recent Findings: An increasing number of studies of larger cohorts, accounting for a variety of variables and often utilizing EEG with standardized terminology, are assessing the prevalence of seizures in hospitalized patients with acute COVID-19 infections, and gaining insight into the prevalence of seizures and their effect on outcomes. Additionally, recent studies are evaluating the effect of the pandemic on PWE, barriers faced, and the usefulness of telehealth. Although there is still much to learn regarding COVID-19, current studies help in assessing the risk of seizures, guiding EEG utilization, and optimizing the use of telehealth during the pandemic., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: A phase I feasibility study to determine the accuracy of identifying seizures based on audio recordings., Methods: We systematically generated 166 audio clips of 30 s duration from 83 patients admitted to an epilepsy monitoring unit between 1/2015 and 12/2016, with one clip during a seizure period and one clip during a non-seizure control period for each patient. Five epileptologists performed a blinded review of the audio clips and rated whether a seizure occurred or not, and indicated the confidence level (low or high) of their rating. The accuracy of individual and consensus ratings were calculated., Results: The overall performance of the consensus rating between the five epileptologists showed a positive predictive value (PPV) of 0.91 and a negative predictive value (NPV) of 0.66. The performance improved when confidence was high (PPV of 0.96, NPV of 0.70). The agreement between the epileptologists was moderate with a kappa of 0.584. Hyperkinetic (PPV 0.92, NPV 0.86) and tonic-clonic (PPV and NPV 1.00) seizures were most accurately identified. Seizures with automatisms only and non-motor seizures could not be accurately identified. Specific seizure-related sounds associated with accurate identification included disordered breathing (PPV and NPV 1.00), rhythmic sounds (PPV 0.93, NPV 0.80), and ictal vocalizations (PPV 1.00, NPV 0.97)., Conclusion: This phase I feasibility study shows that epileptologists are able to accurately identify certain seizure types from audio recordings when the seizures produce sounds. This provides guidance for the development of audio-based seizure detection devices and demonstrate which seizure types could potentially be detected., Competing Interests: Declaration of Competing Interest Author DF receives salary support for consulting and clinical trial related activities performed on behalf of The Epilepsy Study Consortium, a non-profit organization. DF receives no personal income for these activities. NYU receives a fixed amount from the Epilepsy Study Consortium towards DF’s salary. Within the past year, The Epilepsy Study Consortium received payments for research services performed by DF from: Adamas, Axcella, Biogen, Crossject, Engage Pharmaceuticals, Eisai, GW Pharmaceuticals, Pfizer, SK Life Science, Takeda, Xenon, and Zynerba. DF has also served as a paid consultant for Eisai. DF has received travel support from Medtronics, Eisai and the Epilepsy Foundation. DF receives research support from the CDC, NINDS, Epilepsy Foundation, Epitel, and Neuropace. DF serves on the scientific advisory board for Receptor Life Sciences. DF holds equity interests in Neuroview Technology and Receptor Life Sciences. Author RSF has done consulting for Medtronic and has stock options in Smart-Watch, Avails Medical, Cerebral Therapeutics, Zeto, Irody, Eysz. Author PD receives research support from the NIH and NeuroPace, Inc. PD has received honoraria for educational materials from NeuroPace, Inc. and travel reimbursement from Medtronic and NeuroPace, Inc. The remaining authors have no conflicts of interest., (Copyright © 2020 British Epilepsy Association. All rights reserved.)