On-demand EEG education through competition: learning to identify IEDs

**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.