Interictal epileptiform discharge detection in EEG in different practice settings

Jonathan J. Halford, M. Brandon Westover, Suzette M. LaRoche, Micheal P. Macken, Ekrem Kutluay, Jonathan C. Edwards, Leonardo Bonilha, Giridhar P. Kalamangalam, Kan Ding, Jennifer L. Hopp, Amir Arain, Rachael A. Dawson, Gabriel U. Martz, Bethany J. Wolf, Chad G. Waters, Brian C. Dean

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Objective: The goal of the study was to measure the performance of academic and private practice (PP) neurologists in detecting interictal epileptiform discharges in routine scalp EEG recordings. Methods: Thirty-five EEG scorers (EEGers) participated (19 academic and 16 PP) and marked the location of ETs in 200 30-second EEG segments using a web-based EEG annotation system. All participants provided board certification status, years of Epilepsy Fellowship Training (EFT), and years in practice. The Persyst P13 automated IED detection algorithm was also run on the EEG segments for comparison. Results: Academic EEGers had an average of 1.66 years of EFT versus 0.50 years of EFT for PP EEGers (P < 0.0001) and had higher rates of board certification. Inter-rater agreement for the 35 EEGers was fair. There was higher performance for EEGers in academics, with at least 1.5 years of EFT, and with American Board of Clinical Neurophysiology and American Board of Psychiatry and Neurology-E specialty board certification. The Persyst P13 algorithm at its default setting (perception value = 0.4) did not perform as well at the EEGers, but at substantially higher perception value settings, the algorithm performed almost as well human experts. Conclusions: Inter-rater agreement among EEGers in both academic and PP settings varies considerably. Practice location, years of EFT, and board certification are associated with significantly higher performance for IED detection in routine scalp EEG. Continued medical education of PP neurologists and neurologists without EFT is needed to improve routine scalp EEG interpretation skills. The performance of automated detection algorithms is approaching that of human experts.

Original languageEnglish (US)
Pages (from-to)375-380
Number of pages6
JournalJournal of Clinical Neurophysiology
Volume35
Issue number5
DOIs
StatePublished - 2018

Keywords

  • EEG
  • Epilepsy
  • Inter-rater agreement

ASJC Scopus subject areas

  • Physiology
  • Neurology
  • Clinical Neurology
  • Physiology (medical)

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