Monitoring burst suppression in critically ill patients: Multi-centric evaluation of a novel method. Issue 4 (April 2016)

• Record Type: Journal Article
• Authors: Fürbass, Franz; Herta, Johannes; Koren, Johannes; Westover, M. Brandon; Hartmann, Manfred M.; Gruber, Andreas; Baumgartner, Christoph; Kluge, Tilmann
• Is Part Of: Clinical neurophysiology. Volume 127:Issue 4(2016:Apr.)
• Page Start: 2038
• Page End: 2046
• Publication Date: 2016-04
• Subjects: Automatic detection -- Burst suppression pattern -- EEG -- Real-time monitoring -- Periodic pattern; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2016.02.001
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Fully automatic computational method to detect burst suppression patterns in critical care EEG. Insensitivity to EEG artifacts and periodic patterns makes the system suitable for clinical use in real-time patient monitoring. Multi-centric evaluation including the EEG of 88 patients showed high sensitivity and specificity. Abstract: Objective: To develop a computational method to detect and quantify burst suppression patterns (BSP) in the EEGs of critical care patients. A multi-center validation study was performed to assess the detection performance of the method. Methods: The fully automatic method scans the EEG for discontinuous patterns and shows detected BSP and quantitative information on a trending display in real-time. The method is designed to work without setting any patient specific parameters and to be insensitive to EEG artifacts and periodic patterns. For validation a total of 3982 h of EEG from 88 patients were analyzed from three centers. Each EEG was annotated by two reviewers to assess the detection performance and the inter-rater agreement. Results: Average inter-rater agreement between pairs of reviewers was κ = 0.69. On average 22% of the review segments included BSP. An average sensitivity of 90% and a specificity of 84% were measured on the consensus annotations of two reviewers. More than 95% of the periodic patterns in the EEGs were correctly suppressed. Conclusion: A fully automatic method to detect burst suppression patterns was assessed in a multi-center study. The method showed high sensitivity and specificity. Significance: Clinically applicable burst suppression detection method validated in a large multi-center study.