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J Clin Neurophysiol. 2016 Jun;33(3):227-34. doi: 10.1097/WNP.0000000000000278.

Automation of Classical QEEG Trending Methods for Early Detection of Delayed Cerebral Ischemia: More Work to Do.

Author information

1
*Department of Technical Medicine, University of Twente, Enschede, the Netherlands; †Department of Neurology, Comprehensive Epilepsy Center, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; ‡Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, New Haven, Connecticut, U.S.A.; and §Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A.

Abstract

The purpose of this study is to evaluate automated implementations of continuous EEG monitoring-based detection of delayed cerebral ischemia based on methods used in classical retrospective studies. We studied 95 patients with either Fisher 3 or Hunt Hess 4 to 5 aneurysmal subarachnoid hemorrhage who were admitted to the Neurosciences ICU and underwent continuous EEG monitoring. We implemented several variations of two classical algorithms for automated detection of delayed cerebral ischemia based on decreases in alpha-delta ratio and relative alpha variability. Of 95 patients, 43 (45%) developed delayed cerebral ischemia. Our automated implementation of the classical alpha-delta ratio-based trending method resulted in a sensitivity and specificity (Se,Sp) of (80,27)%, compared with the values of (100,76)% reported in the classic study using similar methods in a nonautomated fashion. Our automated implementation of the classical relative alpha variability-based trending method yielded (Se,Sp) values of (65,43)%, compared with (100,46)% reported in the classic study using nonautomated analysis. Our findings suggest that improved methods to detect decreases in alpha-delta ratio and relative alpha variability are needed before an automated EEG-based early delayed cerebral ischemia detection system is ready for clinical use.

PMID:
27258446
PMCID:
PMC4894333
DOI:
10.1097/WNP.0000000000000278
[Indexed for MEDLINE]
Free PMC Article

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