Seizure detection software used to complement the visual screening process for long-term EEG monitoring.
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...
| Publicado en: | American Journal of Electroneurodiagnostic Technology Vol. 50; no. 2; pp. 133 - 148 |
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| Autores principales: | , , , , , |
| Formato: | case study CEU tracings Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105046833&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105046833 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1086508X GJ9 jtl: American Journal of Electroneurodiagnostic Technology issn: 1086508X maglogo: N pubinfo: dt: Jun2010 vid: 50 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 105046833 105046833 2010705799 10.1080/1086508x.2010.11079764 105046833 ppf: 133 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Seizure detection software used to complement the visual screening process for long-term EEG monitoring. aug: au: Halford JJ Shiau D Kern RT Stroman CA Kelly KM Sackellares JC affil: Department of Neurosciences, Medical University of South Carolina, Charleston, SC sug: subj: Electroencephalography Epilepsy Diagnosis Software Utilization Education, Continuing (Credit) ab: 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. pubtype: Academic Journal doctype: case study CEU tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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