Interictal Epileptiform Discharge Detection in EEG in Different Practice Settings.
Jonathan J Halford1, M Brandon Westover2, Suzette M LaRoche3
1Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, U.S.A.
Summary
Academic neurologists with more epilepsy fellowship training detected interictal epileptiform discharges better than private practice neurologists. Automated algorithms show promise in improving EEG interpretation accuracy.
Area of Science:
- Neurology
- Neurophysiology
Background:
- Interictal epileptiform discharges (IEDs) are crucial markers in routine scalp EEG for epilepsy diagnosis.
- Performance in detecting IEDs can vary significantly among neurologists.
Purpose of the Study:
- To compare the IED detection performance of academic versus private practice (PP) neurologists.
- To evaluate the influence of training and board certification on IED detection accuracy.
- To compare human performance with an automated IED detection algorithm.
Main Methods:
- 35 neurologists (19 academic, 16 PP) annotated IEDs in 200 EEG segments using a web-based system.
- Data collected included board certification, years of Epilepsy Fellowship Training (EFT), and years in practice.
- The Persyst P13 automated IED detection algorithm was used for comparison.
Main Results:
- Academic neurologists had more EFT (1.66 vs. 0.50 years) and higher board certification rates.
- Inter-rater agreement among neurologists was fair.
- Higher performance was observed in academic neurologists with ≥1.5 years EFT and specific board certifications.
- The automated algorithm's performance improved at higher perception settings, nearing human expert levels.
Conclusions:
- IED detection performance varies considerably among neurologists.
- Practice setting, EFT duration, and board certification significantly impact IED detection accuracy.
- Enhanced medical education is recommended for PP neurologists lacking EFT.
- Automated IED detection algorithms are approaching the performance of human experts.
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