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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Quantitative EEG Analysis for Automated Detection of Nonconvulsive Seizures in Intensive Care Units
Epilepsy & behavior, v 22(1), pp S69-S73
01 Dec 2011
PMID: 22078521
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Due to increased awareness of the high prevalence of nonconvulsive seizures (NCSs) in critically ill patients, continuous EEG monitoring (cEEG) in ICUs is rapidly increasing in use. However, cEEG monitoring is labor intensive; manual review and interpretation of the EEG are impractical in most ICUs. Effective methods to assist in rapid and accurate detection of NCSs would greatly reduce the cost of cEEG 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 a mean sensitivity of 90.4% and a mean false detection rate of 0.066/h. The two commercial detection products performed with low sensitivities (12.9% and 10.1%) and false detection rates of 1.036/h and 0.013/h, 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 NCSs. This study also suggests that currently available seizure detection software does not have sufficient performance for the detection of NCSs in critically ill patients.
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Details
- Title
- Quantitative EEG Analysis for Automated Detection of Nonconvulsive Seizures in Intensive Care Units
- Creators
- J. Chris Sackellares - Optima NeuroscienceDeng-Shan Shiau - Optima NeuroscienceJonathon J. Halford - Medical University of South CarolinaSuzette M. LaRoche - Emory UniversityKevin M. Kelly - Drexel University
- Publication Details
- Epilepsy & behavior, v 22(1), pp S69-S73
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Neurology
- Web of Science ID
- WOS:000298067800011
- Scopus ID
- 2-s2.0-83455172736
- Other Identifier
- 991019168232704721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Behavioral Sciences
- Clinical Neurology
- Psychiatry