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Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation
Author(s) -
Jin Jing,
Haoqi Sun,
Jennifer A. Kim,
Aline Herlopian,
Ioannis Karakis,
Marcus Ng,
Jonathan J. Halford,
Douglas Maus,
Fonda Chan,
Marjan Dolatshahi,
Carlos Muniz,
Catherine J. Chu,
Valeria Saccà,
Jay Pathmanathan,
Wendong Ge,
Justin Dauwels,
Alice Lam,
Andrew J. Cole,
Sydney S. Cash,
M. Brandon Westover
Publication year - 2019
Publication title -
jama neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.298
H-Index - 231
eISSN - 2168-6157
pISSN - 2168-6149
DOI - 10.1001/jamaneurol.2019.3485
Subject(s) - electroencephalography , interpretation (philosophy) , epilepsy , neuroscience , psychology , medicine , artificial intelligence , computer science , programming language
Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability.

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