Published on Wed Jun 30 2021

Deep active learning for Interictal Ictal Injury Continuum EEG patterns.

Wendong Ge, Jin Jing, Sungtae An, Aline Herlopian, Marcus Ng, Aaron F Struck, Brian Appavu, Emily L Johnson, Gamaleldin Osman, Hiba A Haider, Ioannis Karakis, Jennifer A Kim, Jonathan J Halford, Monica B Dhakar, Rani A Sarkis, Christa B Swisher, Sarah Schmitt, Jong Woo Lee, Mohammad Tabaeizadeh, Andres Rodriguez, Nicolas Gaspard, Emily Gilmore, Susan T Herman, Peter W Kaplan, Jay Pathmanathan, Shenda Hong, Eric S Rosenthal, Sahar Zafar, Jimeng Sun, M Brandon Westover

Seizures and seizure-like electroencephalography (EEG) patterns are commonly encountered in critically ill patients. Automated detection is important for patient care and to enable research. Training accurate detectors requires a large labeled dataset.

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Abstract

Seizures and seizure-like electroencephalography (EEG) patterns, collectively referred to as "ictal interictal injury continuum" (IIIC) patterns, are commonly encountered in critically ill patients. Automated detection is important for patient care and to enable research. However, training accurate detectors requires a large labeled dataset. Active Learning (AL) may help select informative examples to label, but the optimal AL approach remains unclear.