On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges

Jaden D. Barfuss, Fábio A. Nascimento, Erik Duhaime, Srishti Kapur, Ioannis Karakis, Marcus Ng, Aline Herlopian, Alice Lam, Douglas Maus, Jonathan J. Halford, Sydney Cash, M. Brandon Westover, Jin Jing

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG. Methods: We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings. Results: Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice. Conclusions: Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback. Significance: This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.

Original languageEnglish
Pages (from-to)177-186
Number of pages10
JournalClinical Neurophysiology Practice
Volume8
DOIs
StatePublished - Jan 2023

Keywords

  • Education
  • EEG
  • Epilepsy
  • Interictal epileptiform discharges

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