Web-based collection of expert opinion on routine scalp EEG: software development and interrater reliability.
Computerized detection of epileptiform transients (ETs), characterized by interictal spikes and sharp waves in the EEG, has been a research goal for the last 40 years. A reliable method for detecting ETs would assist physicians in interpretation and improve efficiency in reviewing long-term EEG reco...
| Publicado en: | Journal of Clinical Neurophysiology Vol. 28; no. 2; pp. 178 - 185 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
2011 Apr
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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=104900861&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104900861 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07360258 8CL jtl: Journal of Clinical Neurophysiology issn: 07360258 maglogo: N pubinfo: dt: 2011 Apr vid: 28 iid: 2 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 104900861 104900861 2011055752 10.1097/WNP.0b013e31821215e3 NLM21399515 104900861 ppf: 178 ppct: 7 formats: tig: atl: Web-based collection of expert opinion on routine scalp EEG: software development and interrater reliability. aug: au: Halford JJ Pressly WB Benbadis SR Tatum WO 4th Turner RP Arain A Pritchard PB Edwards JC Dean BC affil: Department of Neurosciences, Medical University of South Carolina, 96 Jonathan Lucas Street, Charleston, SC 29425, USA. halfordj@musc.edu sug: subj: Brain Physiopathology Diagnosis, Computer Assisted Electroencephalography Methods Epilepsy Diagnosis Expert Systems Signal Processing, Computer Assisted Software Algorithms Artifacts Epilepsy Physiopathology Human Internet Observer Bias Predictive Value of Tests Reproducibility of Results Scalp Time Factors User-Computer Interface ab: Computerized detection of epileptiform transients (ETs), characterized by interictal spikes and sharp waves in the EEG, has been a research goal for the last 40 years. A reliable method for detecting ETs would assist physicians in interpretation and improve efficiency in reviewing long-term EEG recordings. Computer algorithms developed thus far for detecting ETs are not as reliable as human experts, primarily due to the large number of false-positive detections. Comparing the performance of different algorithms is difficult because each study uses individual EEG test datasets. In this article, we present EEGnet, a distributed web-based platform for the acquisition and analysis of large-scale training datasets for comparison of different EEG ET detection algorithms. This software allows EEG scorers to log in through the web, mark EEG segments of interest, and categorize segments of interest using a conventional clinical EEG user interface. This software platform was used by seven board-certified academic epileptologists to score 40 short 30-second EEG segments from 40 patients, half containing ETs and half containing artifacts and normal variants. The software performance was adequate. Interrater reliability for marking the location of paroxysmal activity was low. Interrater reliability of marking artifacts and ETs was high and moderate, respectively. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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