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Author Notes:

J. Chris Sackellares, M.D., Optima Neuroscience, Inc., 13420 Progress Blvd., Suit 200, Alachua, FL 32615, USA, Tel: +1 352-371-8281, Fax: +1 386-462-0606, csackellares@optimaneuro.com



  • Science & Technology
  • Life Sciences & Biomedicine
  • Behavioral Sciences
  • Clinical Neurology
  • Psychiatry
  • Neurosciences & Neurology
  • Intensive care unit
  • Nonconvulsive seizures
  • Continuous electroencephalographic monitoring
  • Seizure detection
  • Quantitative EEG trending
  • ICU

Quantitative EEG analysis for automated detection of nonconvulsive seizures in intensive care units


Journal Title:

Epilepsy and Behavior


Volume 22, Number SUPPL. 1


, Pages S69-S73

Type of Work:

Article | Post-print: After Peer Review


Because of increased awareness of the high prevalence of nonconvulsive seizures in critically ill patients, use of continuous EEG (cEEG) monitoring is rapidly increasing in ICUs. However, cEEG monitoring is labor intensive, and manual review and interpretation of the EEG are impractical in most ICUs. Effective methods to assist in rapid and accurate detection of nonconvulsive seizures would greatly reduce the cost of cEEG monitoring and enhance the quality of patient care. In this study, we report a preliminary investigation of a novel ICU EEG analysis and seizure detection algorithm. Twenty-four prolonged cEEG recordings were included in this study. Seizure detection sensitivity and specificity were assessed for the new algorithm and for the two commercial seizure detection software systems. The new algorithm performed with a mean sensitivity of 90.4% and a mean false detection rate of 0.066/hour. The two commercial detection products performed with low sensitivities (12.9 and 10.1%) and false detection rates of 1.036/hour and 0.013/hour, respectively. These findings suggest that the novel algorithm has potential to be the basis of clinically useful software that can assist ICU staff in timely identification of nonconvulsive seizures. This study also suggests that currently available seizure detection software does not perform sufficiently in detection of nonconvulsive seizures in critically ill patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Copyright information:

© 2011 Elsevier Inc.All rights reserved.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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