• J Clin Neurophysiol · Aug 2015

    Diagnostic Accuracy of Electrographic Seizure Detection by Neurophysiologists and Non-Neurophysiologists in the Adult ICU Using a Panel of Quantitative EEG Trends.

    • Christa B Swisher, Corey R White, Brian E Mace, Keith E Dombrowski, Aatif M Husain, Bradley J Kolls, Rodney R Radtke, Tung T Tran, and Saurabh R Sinha.
    • *Department of Neurology, Duke University Medical Center, Durham, North Carolina, U.S.A.; and †Neurodiagnostic Center, Veterans Affairs Medical Center, Durham, North Carolina, U.S.A.
    • J Clin Neurophysiol. 2015 Aug 1; 32 (4): 324-30.

    PurposeTo evaluate the sensitivity and specificity of a panel of quantitative EEG (qEEG) trends for seizure detection in adult intensive care unit (ICU) patients when reviewed by neurophysiologists and non-neurophysiologists.MethodsOne hour qEEG panels (n = 180) were collected retrospectively from 45 ICU patients and were distributed to 5 neurophysiologists, 7 EEG technologists, and 5 Neuroscience ICU nurses for evaluation of seizures. Each panel consisted of the following qEEG tools, displayed separately for left and right hemisphere electrodes: rhythmicity spectrogram (rhythmic run detection and display; Persyst Inc), color density spectral array, EEG asymmetry index, and amplitude integrated EEG. The reviewers did not have access to the raw EEG data.ResultsFor the reviewer's ability to detect the presence of seizures on qEEG panels when compared with the gold standard of independent raw EEG review, the sensitivities and specificities are as follows: neurophysiologists 0.87 and 0.61, EEG technologists 0.80 and 0.80, and Neuroscience ICU nurses 0.87 and 0.61, respectively. There was no statistical difference among the three groups regarding sensitivity.ConclusionsQuantitative EEG display panels are a promising tool to aid detection of seizures by non-neurophysiologists as well as by neurophysiologists. However, even when used as a panel, qEEG trends do not appear to be adequate as the sole method for reviewing continuous EEG data.

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