664 EEG study in disruptive behavior disorder and ADHD

Abstract

Video-EEG monitoring is the ultimate way to diagnose non-convulsive status epilepticus (NCSE) in intensive care units (ICU). Usually EEG recordings are evaluated once a day by an electrophysiologist, which may lead to delay in diagnosis. Digital EEG trend analysis methods like amplitude integrated EEG (aEEG) and density spectral array (DSA) have been developed to facilitate recognition of seizures. In this study, we aimed to investigate the diagnostic utility of these methods by non-expert physicians and ICU nurses for NCSE identification in an adult neurological ICU. Ten patients with NCSE and ten control patients without seizures were included in the study. The raw EEG recordings of all subjects were converted to both aEEG and DSA and displayed simultaneously without conventional EEG. After training for seizure recognition with both methods, two physicians and two nurses analyzed the visual displays individually, and marked seizure timings. Their results were compared with those of a study epileptologist. Participants analyzed 615 h of EEG data with 700 seizures. Overall, 63% of the seizures were recognized by all, 15.6% by three, 11.6% by two, 8.3% by one rater and only 1.5% were missed by all of them (sensitivity was 88–99%, and specificity was 89–95% when the ratings were assessed as 1-h epochs). False positive rates were 1 per 2 h in the study and 1 per 6 h in the control groups. Interrater agreement was high (κ = 0.79–0.81). Bilateral independent seizures and ictal recordings with lower amplitude and shorter duration were more likely to be missed. There was no difference in performance between the rating of physicians and nurses. Our study demonstrates that bedside nurses, ICU fellows and residents can achieve acceptable level of accuracy for seizure identification using the digital EEG trend analysis methods following brief training. This may help earlier notification of the electrophysiologist who is not always available in ICUs.