Abstract Details

Title On Demand EEG ��ɫ�� - a Novel, App-based Teaching Method Focused on Interictal Epileptiform Discharge Identification

Topic Epilepsy/Clinical Neurophysiology (EEG)

Presentation(s) P1 - Poster Session 1 (9:00 AM-5:00 PM)

Poster/Presentation
Number 163

Objective

To evaluate the effectiveness of a novel educational platform aimed at teaching how to identify interictal epileptiform discharges (IEDs) on EEG.

Background Given the current challenges related to delivering optimal EEG education to neurology residents, supplemental EEG teaching methods are needed. Ensuring that neurology residents can confidently and independently interpret EEGs by graduation is of paramount importance given that a large portion of EEGs in the US are read by neurologists without fellowship training in clinical neurophysiology or epilepsy.

Design/Methods

We created a public EEG test within the iOS app DiagnosUs to test a novel method for learning to identify IEDs. The test draws from a pool of 13,262 EEGs with candidate IEDs, with answers determined by consensus of 8 experts. Users were shown a single EEG epoch with a marked candidate IED and asked to rate this sharp transient as epileptiform (IED) or not (non-IED). Learning was analyzed by fitting a simple parametric curve p(n) = a(n/N)^b to the sequence of each user’s responses. An “expert level” accuracy was established as 90% based on performance of nine additional experts who had rated a similar pool of candidate IEDs. To further characterize users’ learning, we analyzed the associations between quantitative IED features and the degree of consensus between experts and general users.

Results According to our learning model, users had a mean improvement of 13% over 1,000 questions and an ending mean accuracy of 81%. Notably, 10% of users reached an accuracy above the established “expert level”. Further, we found that users and experts seem to rely on similar IED features when analyzing candidate IEDs.

Conclusions

This app-based learning activity has a great potential to be an effective EEG education tool for IED identification. This may be a valuable supplemental education EEG tool to teach neurology trainees how to accurately identify IEDs on EEG.

Authors/Disclosures
PRESENTER	No disclosure on file
	No disclosure on file
	No disclosure on file
M. B. Westover, MD, PhD (MGH)	Dr. Westover has received personal compensation in the range of $50,000-$99,999 for serving as a Consultant for Beacon Biosignals. Dr. Westover has stock in Beacon Biosignals. The institution of Dr. Westover has received research support from NIH. Dr. Westover has received publishing royalties from a publication relating to health care. Dr. Westover has a non-compensated relationship as a cofounder with Beacon Biosignals that is relevant to AAN interests or activities.
Jin Jing	No disclosure on file

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