• F Fürbass, M M Hartmann, J J Halford, J Koren, J Herta, A Gruber, C Baumgartner, and T Kluge.
• Austrian Institute of Technology GmbH (AIT), Safety & Security Department, Vienna, Austria. Electronic address: franz.fuerbass@ait.ac.at.
• Neurophysiol Clin. 2015 Sep 1; 45 (3): 203-13.

Aims Of The StudyContinuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports.Materials And MethodsFirst of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report.ResultsIn three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures.ConclusionThe use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies.Copyright © 2015 Elsevier Masson SAS. All rights reserved.

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