Journal article
Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study
ZS Chen, A Hsieh, G Sun, GK Bergey, SF Berkovic, P Perucca, W D'Souza, CJ Elder, P Farooque, EL Johnson, S Barnard, R Nightscales, P Kwan, B Moseley, TJ O'Brien, S Sivathamboo, J Laze, D Friedman, O Devinsky, DC Hesdorffer Show all
Frontiers in Neurology | FRONTIERS MEDIA SA | Published : 2022
Abstract
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..
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Funding Acknowledgements
Funding This study was funded by grants from the US National Institute of Neurological Disorders and Stroke (NINDS, R01-NS123928, R01-NS121776), the National Institute of Mental Health (NIMH, R01-MH118928) and National Science Foundation (NSF, CBET-1835000), the Multidisciplinary University Research Initiatives (MURI), the Centers for Disease Control and Prevention (CDC), the Finding a Cure for Epilepsy and Seizures (FACES), and the Oracle for Research Award. AH received a GLASS (Global Leaders and Scholars in STEM) funding from NYU Tandon School of Engineering. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.