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Background and Objectives: To compare specific life stressors and domestic abuse that pregnant women and others with epilepsy (WWE) experience compared with pregnant women and others without epilepsy (WWoE)., Methods: The Pregnancy Risk Assessment Monitoring System (PRAMS) is an annual weighted survey of randomly sampled postpartum women administered by the Centers for Disease Control and Prevention. We used data from the PRAMS from 2012 to 2020 in 13 states to assess the life stressors reported by WWE compared with WWoE. We adjusted the data for maternal age, race, ethnicity, marital status, education, and socioeconomic status (SES; using income, Women, Infants, and Children program [WIC], and Medicaid use). We also examined reported abuse in WWE compared with WWoE., Results: This study included data from 64,951 postpartum women, representing 4,072,189 women through weighted sampling. Of these, 1,140 reported having a diagnosis of epilepsy in the 3 months before their pregnancies (representing 81,021 WWE). WWE experienced a higher number of stressors compared with WWoE. WWE were more likely to have experienced 9 of the 14 stressors asked in the PRAMS questionnaire: severe illness of a close family member, separation or divorce, homelessness, loss of a partner's job, cut in work hours or pay, arguing more than usual with their partner, serving jail time, substance abuse problem in a close contact, and death of a close contact. After adjusting for demographics (age, race, and SES), epilepsy was still associated with a higher number of stressors in pregnant women. Other factors associated with stressors were younger age, Indigenous or mixed race, non-Hispanic ethnicity, lower income, and WIC or Medicaid use. Those who were married were less likely to report stressors. WWE were also more likely to report abuse before or during their pregnancies., Discussion: Although managing stress is important in both epilepsy and pregnancy, WWE experience more stressors than do WWoE. After adjusting for maternal age, race, and SES, this increase in stressors persisted. Women who were younger, with lower income, on WIC or Medicaid, or not married were also more likely to experience life stressors. Alarmingly, reported abuse was also higher in WWE compared with WWoE. Attention from clinicians and support services for WWE are needed to optimize good pregnancy outcomes., (© 2023 American Academy of Neurology.)

Objective: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls., Methods: This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve., Results: The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction., Conclusions: Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies., Competing Interests: ZC reports grants from the National Institutes of Health (NIH) and National Science Foundation (NSF) during the conduct of the study. ZC is also a founder and CEO of NeuroThX, LLC. ZC also received cloud computing resources supported by the Oracle for Research Award. 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 toward DF salary. Within the past two years, The Epilepsy Study Consortium received payments for research services performed by DF from: Alterity, Baergic, Biogen, BioXcell, Cerevel, Cerebral, Jannsen, Lundbeck, Neurocrine, SK Life Science, and Xenon. He has also served as a paid consultant for Neurelis Pharmaceuticals and Receptor Life Sciences. He has received travel support from the Epilepsy Foundation. He has received research support from NINDS, Epilepsy Foundation, Empatica, Epitel, UCB, Inc and Neuropace unrelated to this study. He serves on the scientific advisory board for Receptor Life Sciences. He holds equity interests in Neuroview Technology. He received royalty income from Oxford University Press. SB is supported by a Program Grant from the National Health and Medical Research Council of Australia (APP1091593). He reports grants from Eisai, UCB Pharma, and SciGen; has a patent for SCN1A licensed to various diagnostic companies with no financial return, a patent for PRRT2 gene licensed to Athena Diagnostics, and a patent for Diagnostic and Therapeutic Methods for Epilepsy and Mental Retardation Limited to Females (EFMR) licensed to Athena Diagnostics. PP is supported by an Early Career Fellowship from the National Health and Medical Research Council (APP1163708), the Epilepsy Foundation, The University of Melbourne, Monash University, Brain Australia, and the Weary Dunlop Medical Research Foundation. He has received speaker honoraria or consultancy fees to his institution from Chiesi, Eisai, the limbic, LivaNova, Novartis, Sun Pharma, Supernus, and UCB Pharma. He is an Associate Editor for Epilepsia Open. WD'S receives salary support from The University of Melbourne. He has received travel, investigator-initiated, scientific advisory board and speaker honoraria from UCB Pharma Australia & Global; investigator-initiated, scientific advisory board, travel and speaker honoraria from Eisai Australia & Global; advisory board honoraria from Liva Nova; educational grants from Novartis Pharmaceuticals, Pfizer Pharmaceuticals and Sanofi-Synthelabo; educational; travel and fellowship grants from GSK Neurology Australia, and honoraria from SciGen Pharmaceuticals. He has shareholdings in the device company EpiMinder. PK is supported by a Medical Research Future Fund from the National Health and Medical Research Council of Australia (MRF1136427) and the Victorian Medical Research Acceleration Fund. He reports grants and personal fees from Eisai, UCB Pharma, and LivaNova; reports grants from Zynerba, Biscayne, and GW Pharmaceuticals; and has received travel, speaker honoraria, or consultancy fees from Sun Pharmaceuticals, Supernus Pharmaceuticals, Novartis, and Eisai. BM is a paid employee at Clinical Development Neurocrine Biosciences Inc. BM has previously served as an advisory board member/consultant for Eisai and UCB Pharma and as a speaker for Eisai, LivaNova and UCB Pharma. He has previously received research support from GW Pharma, LivaNova, Nonin Medical, Inc, Sunovion and Xenon Pharmaceuticals. TO'B is supported by a Program Grant (APP1091593) and Investigator Grant (APP1176426) from the National Health and Medical Research Council of Australia and the Victorian Medical Research Acceleration Fund. He reports grants and consulting fees to his institution from Eisai, UCB Pharma, Praxis, Biogen, ES Therapeutics and Zynerba. SS is supported by a Bridging Postdoctoral Fellowship from Monash University (BPF20-3253672466) and the Victorian Medical Research Acceleration Fund. She reports salary support paid to her institution from Kaoskey and Optalert for clinical trial related activities; she receives no personal income for these activities. OD received grants from the NIH during the conduct of the study, and received funding from Finding A Cure for Epilepsy and Seizures (FACES) and has equity in Empatica.. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Hsieh, Sun, Bergey, Berkovic, Perucca, D'Souza, Elder, Farooque, Johnson, Barnard, Nightscales, Kwan, Moseley, O'Brien, Sivathamboo, Laze, Friedman, Devinsky and The MS-BioS Study Group.)

Objective: To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity., Methods: Our algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity. In this study we compared multiple configurations using a one-class SVM to assess if there is significance over specific neural features or electrode locations., Results: Our results show that the algorithm reaches a sensitivity of 87% for early-onset seizure detection and of 97.7% as a generic seizure detection., Conclusions: Our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false positive rate and robustness in detection of different type of seizure-onset patterns., Significance: This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)