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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Seizure Detection Software Used to Complement the Visual Screening Process for Long-Term EEG Monitoring
American journal of electroneurodiagnostic technology, v 50(2), pp 133-147
01 Jun 2010
PMCID: PMC4674077
PMID: 26658426
Abstract
It is widely recognized that visual screening of long-term EEG recordings can be time-consuming and labor-intensive due to the large volume of patient data produced daily in most Epilepsy Monitoring Units (EMUs). As a result, seizures, especially those with only electrographic changes, are sometimes overlooked, which for some patients could result in missed information for diagnosis, an unnecessarily prolonged hospital stay, and unavailable EMU beds for others. In this report, we propose that a better solution for identifying seizures in long-term EEG recording is to combine detection results from a reliable (high sensitivity and low false detection rate) automated detection system with EEG technologists' visual screening process. Using commercially available detection software, we present case studies that demonstrate potential benefits of this method that could help improve detection rates and bring greater efficiency to the seizure identification process in long-term EEG monitoring.
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6 citations in Scopus
Details
- Title
- Seizure Detection Software Used to Complement the Visual Screening Process for Long-Term EEG Monitoring
- Creators
- Jonathan J. Halford - Medical University of South CarolinaDeng-Shan Shiau - Optima NeuroscienceRyan T. Kern - Optima NeuroscienceConrad A. Stroman - Optima NeuroscienceJ. Chris Sackellares - Allegheny General HospitalKevin M. Kelly - Drexel University
- Publication Details
- American journal of electroneurodiagnostic technology, v 50(2), pp 133-147
- Publisher
- Taylor & Francis
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Neurology
- Scopus ID
- 2-s2.0-77954731866
- Other Identifier
- 991019173662204721