Literature DB >> 35770748

Measuring expertise in identifying interictal epileptiform discharges.

Nitish M Harid, Jin Jing, Jacob Hogan, Fábio A Nascimento, An Ouyang, Wei-Long Zheng, Wendong Ge, Sahar F Zafar, Jennifer A Kim, D Lam Alice, Aline Herlopian, Douglas Maus, Ioannis Karakis, Marcus Ng, Shenda Hong, Zhu Yu, Peter W Kaplan, Sydney Cash, Mouhsin Shafi, Gabriel Martz, Jonathan J Halford, Michael Brandon Westover.   

Abstract

OBJECTIVE: Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills.
METHODS: A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision.
RESULTS: Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE: Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.

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Year:  2022        PMID: 35770748      PMCID: PMC9340812          DOI: 10.1684/epd.2021.1409

Source DB:  PubMed          Journal:  Epileptic Disord        ISSN: 1294-9361            Impact factor:   2.333


  27 in total

1.  Continuous or emergent EEG: can bedside caregivers recognize epileptiform discharges?

Authors:  Enrique C Leira; Mary E Bertrand; R Edward Hogan; Salvador Cruz-Flores; Kathleen W Wyrwich; Osamah J Albaker; Eve M Holzemer
Journal:  Intensive Care Med       Date:  2003-11-13       Impact factor: 17.440

2.  Automatic EEG spike detection: what should the computer imitate?

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3.  Overintepretation of EEGs and misdiagnosis of epilepsy.

Authors:  Selim R Benbadis; William O Tatum
Journal:  J Clin Neurophysiol       Date:  2003-02       Impact factor: 2.177

4.  The misdiagnosis of epilepsy and the management of refractory epilepsy in a specialist clinic.

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Journal:  QJM       Date:  1999-01

5.  Spike detection II: automatic, perception-based detection and clustering.

Authors:  S B Wilson; C A Turner; R G Emerson; M L Scheuer
Journal:  Clin Neurophysiol       Date:  1999-03       Impact factor: 3.708

6.  A practical guide for routine EEG studies in epilepsy.

Authors:  J Engel
Journal:  J Clin Neurophysiol       Date:  1984-04       Impact factor: 2.177

7.  Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis.

Authors:  Jonathan J Halford; Robert J Schalkoff; Jing Zhou; Selim R Benbadis; William O Tatum; Robert P Turner; Saurabh R Sinha; Nathan B Fountain; Amir Arain; Paul B Pritchard; Ekrem Kutluay; Gabriel Martz; Jonathan C Edwards; Chad Waters; Brian C Dean
Journal:  J Neurosci Methods       Date:  2012-11-19       Impact factor: 2.390

8.  Spike detection. I. Correlation and reliability of human experts.

Authors:  S B Wilson; R N Harner; F H Duffy; B R Tharp; M R Nuwer; M R Sperling
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1996-03

9.  EEG interpretation reliability and interpreter confidence: a large single-center study.

Authors:  Arthur C Grant; Samah G Abdel-Baki; Jeremy Weedon; Vanessa Arnedo; Geetha Chari; Ewa Koziorynska; Catherine Lushbough; Douglas Maus; Tresa McSween; Katherine A Mortati; Alexandra Reznikov; Ahmet Omurtag
Journal:  Epilepsy Behav       Date:  2014-02-13       Impact factor: 2.937

10.  A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017.

Authors:  Nick Kane; Jayant Acharya; Sandor Benickzy; Luis Caboclo; Simon Finnigan; Peter W Kaplan; Hiroshi Shibasaki; Ronit Pressler; Michel J A M van Putten
Journal:  Clin Neurophysiol Pract       Date:  2017-08-04
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