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Voltage-based automated detection of postictal generalized electroencephalographic suppression: Algorithm development and validation
Affiliation:1. Department of Internal Medicine, University of California, Irvine School of Medicine, Orange, CA, USA;2. Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA;3. Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA;4. Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA;5. Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA;6. Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA;7. Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA;8. Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA;9. Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA;1. University of New Mexico, Laboratories of Parasitic and Viral Diseases, Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya;2. Department of Medical Biochemistry, Maseno University, Maseno, Kenya;3. Center for Global Health, University of New Mexico, Albuquerque, NM, USA;4. Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya;5. Department of Psychology, College of Charleston, Charleston, SC, USA;1. Unidad de Infectología, Hospital General de Agudos Cosme Argerich, Buenos Aires, Argentina;2. Servicio de Neonatología, Hospital General de Agudos Cosme Argerich, Buenos Aires, Argentina;1. Division of Neurology, Department of Pediatrics, Nationwide Children''s Hospital/The Ohio State University, Columbus, Ohio;2. Division of Neurology, Department of Pediatrics, Cincinnati Children''s Hospital Medical Center/University of Cincinnati, Cincinnati, Ohio;1. Neuroview Technology, United States;2. Department of Neurology and Neurosurgery, NYU Langone School of Medicine, New York, United States;3. Department of Neurology, Zucker Hofstra School of Medicine, New York, United States
Abstract:ObjectivePostictal generalized electroencephalographic suppression (PGES) is a pattern of low-voltage scalp electroencephalographic (EEG) activity following termination of generalized seizures. PGES has been associated with both sudden unexplained death in patients with epilepsy and therapeutic efficacy of electroconvulsive therapy (ECT). Automated detection of PGES epochs may aid in reliable quantification of this phenomenon.MethodsWe developed a voltage-based algorithm for detecting PGES. This algorithm applies existing criteria to simulate expert epileptologist readings. Validation relied on postictal EEG recording from patients undergoing ECT (NCT02761330), assessing concordance among the algorithm and four clinical epileptologists.ResultsWe observed low-to-moderate concordance among epileptologist ratings of PGES. Despite this, the algorithm displayed high discriminability in comparison to individual epileptologists (C-statistic range: 0.86–0.92). The algorithm displayed high discrimination (C-statistic: 0.91) and substantial peak agreement (Cohen’s Kappa: 0.65) in comparison to a consensus of clinical ratings. Interrater agreement between the algorithm and individual epileptologists was on par with that among expert epileptologists.ConclusionsAn automated voltage-based algorithm can be used to detect PGES following ECT, with discriminability nearing that of experts.SignificanceAlgorithmic detection may support clinical readings of PGES and improve precision when correlating this marker with clinical outcomes following generalized seizures.
Keywords:Postictal generalized EEG suppression  Electroconvulsive Therapy (ECT)  Sudden unexplained death in epilepsy  Seizure  Major depressive disorder  Algorithms
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