Optimized set of criteria for defining interictal epileptiform EEG discharges.

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.

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

OBJECTIVE: To 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. METHODS: Seven 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. RESULTS: Cutoff 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. CONCLUSIONS: The 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.