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Objective To find and validate the optimal combination of criteria that define interictal epileptiform EEG discharges (IEDs). Our target was a specificity over 95%, to avoid over-reading in clinical EEG. Methods We constructed 63 combinations of the six criteria from the operational definition of IEDs, recently issued in the EEG-glossary of the International Federation of Clinical Neurophysiology (IFCN). The diagnostic gold standard was derived from video-EEG recordings. In a testing EEG dataset from 100 patients, we selected the best performing combinations of criteria and then we validated them in an independent dataset from 70 patients. We compared their performance with subjective, expert-scorings and we determined inter-rater agreement (IRA). Results Without using criteria, the specificity of expert-scorings was lower than the pre-defined threshold (86%). The best performing combination of criteria was the following: waves with spiky morphology, followed by a slow-afterwave and voltage map suggesting a source in the brain. In the validation dataset this achieved a specificity of 97% and a sensitivity of 89%. IRA was substantial. Conclusions The optimized set of criteria for defining IEDs has high accuracy and IRA. Significance Using these criteria will contribute to decreasing over-reading of EEG and avoid misdiagnosis of epilepsy.

ObjectiveTo define and validate criteria for accurate identification of EEG interictal epileptiform discharges (IEDs) using (1) the 6 sensor space criteria proposed by the International Federation of Clinical Neurophysiology (IFCN) and (2) a novel source space method. Criteria yielding high specificity are needed because EEG over-reading is a common cause of epilepsy misdiagnosis.MethodsSeven raters reviewed EEG sharp transients from 100 patients with and without epilepsy (diagnosed definitively by video-EEG recording of habitual events). Raters reviewed the transients, randomized, and classified them as epileptiform or nonepileptiform in 3 separate rounds: in 2, EEG was reviewed in sensor space (scoring the presence/absence of each IFCN criterion for each transient or classifying unrestricted by criteria [expert scoring]); in the other, review and classification were performed in source space.ResultsCutoff 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 cutoff and analysis in source space (both 95.65%); the sensitivity of these methods was 81.48% and 85.19%, respectively.ConclusionsThe 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 evidenceThis 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.

Background Presence of Epileptiform Discharges (ED) in EEG recordings are highly associated with an epilepsy diagnosis. The International Federation of Clinical Neurophysiology (IFCN) suggested 6 criteria in EEG sensor-space, so that presence of at least 4 of them defines a waveform as ED. The aim of our study to assess inter-rater agreement (IRA) and accuracy of identifying spikes in sensor space (using the IFCN criteria at different thresholds) and in source space. Methods EEG samples from 100 consecutive patients with epilepsy and non-epileptic paroxysmal events were reviewed separately in sensor-space and source-space, in different, randomized order, by 7 raters, who scored the presence/absence of each IFCN-criterion in sensor-space, and presence/absence of EDs in source-space. EDs were defined in sensor-space using different thresholds (2–6 criteria) and without counting criteria. Accuracy was determined for each rater and for the majority decisions for each sample. Material and results IRA was moderate both in sensor space and in source space (k: 0.49–0.60). In sensor-space, the highest accuracy (91%) and sensitivity (96%) was achieved using a threshold of 4 criteria. However, this gave a specificity that was lower than acceptable for EEG (85%). Using a threshold of 5 criteria in sensor-space specificity of 96% was achieved, at a sensitivity of 83% and accuracy of 89%. Results in source-space were identical to using the threshold of 5 criteria in sensor-space. Conclusions Using a threshold of 5 IFCN-criteria in sensor-space, and using source-space yields a high specificity and acceptable sensitivity for identifying EDs.