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Criteria for defining interictal epileptiform discharges in EEG: a clinical validation study

Citation

Kural, Mustafa Aykut et al. (2020), Criteria for defining interictal epileptiform discharges in EEG: a clinical validation study, Dryad, Dataset, https://doi.org/10.5061/dryad.xsj3tx99w

Abstract

Objective: To define and validate criteria for accurate identification of EEG interictal epileptiform discharges (IEDs) using: (a) the six sensor space criteria proposed by the International Federation of Clinical Neurophysiology (IFCN), and, (b) a novel source space method. Criteria yielding high specificity are needed because EEG “over-reading” is a common cause of epilepsy misdiagnosis.

Methods: Seven raters reviewed EEG segments containing sharp waveforms from 100 patients with and without epilepsy. Clinical diagnosis gold standard was video-EEG recording of habitual paroxysmal events. Raters reviewed in three separate rounds, in randomized order: 1) in sensor space, presence/absence of each IFCN criterion was scored; 2) in source space, sharp transients were classified as epileptiform or non-epileptiform; 3) in sensor space, sharp transients were classified unrestricted by any criteria (expert scoring).

Results: Cut-off values of 4 and 5 criteria in sensor space, and analysis in source space, provided high accuracy (91%, 88% and 90%, respectively), similar to expert scoring (92%). Two methods had specificity exceeding the desired threshold of 95%: using 5 IFCN criteria as cut-off, and analysis in source space (both 95.65%); sensitivity of these methods was 81.48% and 85.19%.

Conclusions: Presence of 5 IFCN criteria in sensor space and analysis in source space are optimal for clinical implementation. By extracting these objective features, diagnostic accuracy similar to expert scorings is achieved.

Classification of evidence: This study provides Class III evidence that IFCN criteria in sensor space and analysis in source space have high specificity (>95%) and sensitivity (81-85%) for identification of IEDs.

Usage Notes

EDF files are electroencephalography (EEG) recordings in a non-proprietary format (European Data Format).

Most EEG software can read EDF format.