We have previously described the Bergen Epileptiform Morphology Score (BEMS) ( 17), a score from 0 to 86 for sharp transients, where a higher value indicates a more typical epileptiform morphology. Latency to the first IED, a measure analogous to spike count has been examined in long-term EEG recordings ( 16). Spike count has been assessed for specific epilepsy types, such as continuous spike–wave during sleep ( 11, 12), benign epilepsy with centro-temporal spikes ( 13), temporal lobe epilepsy ( 14), and juvenile myoclonic epilepsy ( 15). ( 10) demonstrated that a higher IED count is required to conclude that an EEG contains epileptiform activity when the IEDs have a less typical epileptiform morphology. The literature is sparse regarding spike count in routine scalp EEG. IRA seems to be better for overall EEG interpretation than individual IEDs ( 4, 6– 9). An EEG conclusion should be based not only on a single graphoelement but also on all available data in an EEG recording. It is unclear whether additional EEG phenomena such as IED counts are relevant for a diagnosis of epilepsy. The IED criteria describe the evaluation of single transients without discussing the possible role of recurring sharp transients. Inter-rater agreement (IRA) has been assessed for specific morphological features ( 2, 4), and optimal combinations of the criteria have been validated ( 3). The diagnostic value of morphological IED features has been discussed extensively ( 2– 5). According to the criteria, typical morphological traits of an IED are a spiky peak, a wave duration that is different from the background waves, an asymmetric waveform, a slow after-wave, disrupted background activity, and a dipole suggesting that the source of the transient is in the brain. The definition of epileptiform activity includes qualitative criteria to guide EEG readers in detecting interictal epileptiform discharges (IEDs) ( 1). In the external dataset, the sensitivity for an epileptiform EEG was 60–70%, and the specificity was 90–93%.Ĭonclusion: Quantified EEG spike morphology (BEMS) and IED candidate count can be combined to classify an EEG as epileptiform with high reliability but with lower sensitivity than regular visual EEG review. The sensitivity was 27–37%, and the specificity was 93–97% for a follow-up diagnosis of epilepsy.

These criteria had almost perfect inter-rater reliability (Gwet’s AC1 0.96), reasonable sensitivity of 56–64%, and high specificity of 98–99%. The optimal criteria to classify an EEG as epileptiform were either one spike at BEMS > = 58, two at > = 47, or seven at > = 36. Results: Interictal epileptiform discharge (IED) candidate count and BEMS were moderately correlated.

Diagnostic performance was assessed and then validated in an external dataset. BEMS and IED candidate counts were combined to classify EEGs as epileptiform or non-epileptiform. Three blinded EEG readers marked all IED candidates. Methods: We included 400 consecutive patients from a clinical SCORE EEG database during 2013–2017 who had focal sharp discharges in their EEG, but no previous diagnosis of epilepsy. Purpose: The purpose of this study is to investigate the impact of Bergen Epileptiform Morphology Score (BEMS) and interictal epileptiform discharge (IED) candidate count in EEG classification. 6Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.5Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.4Department of Clinical Neurophysiology, Danish Epilepsy Centre Filadelfia, Dianalund, Denmark.3Department of Neurology, Haukeland University Hospital, Bergen, Norway.2Department of Clinical Medicine, University of Bergen, Bergen, Norway.1Department of Clinical Neurophysiology, Haukeland University Hospital, Bergen, Norway.Eivind Aanestad 1,2 * Nils Erik Gilhus 2,3 Henning Kristian Olberg 1,2 Mustafa Aykut Kural 4,5,6 Sándor Beniczky 4,5,6 Jan Brogger 1

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