Determining the Spike-Wave Index Using Automated Detection Software.
Purpose: The spike-wave index (SWI) is a key feature in the diagnosis of electrical status epilepticus during slow-wave sleep. Estimating the SWI manually is time-consuming and is subject to interrater and intrarater variability. Use of automated detection software would save time. Thereby, this sof...
| Publicado en: | Journal of Clinical Neurophysiology Vol. 38; no. 3; pp. 198 - 202 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
May2021
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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=150396951&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150396951 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07360258 8CL jtl: Journal of Clinical Neurophysiology issn: 07360258 maglogo: N pubinfo: dt: May2021 vid: 38 iid: 3 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 150396951 150396951 NLM31834040 10.1097/WNP.0000000000000672 NLM31834040 150396951 ppf: 198 ppct: 4 formats: tig: atl: Determining the Spike-Wave Index Using Automated Detection Software. aug: au: Reus, Elisabeth E. M. Visser, Gerhard H. Cox, Fieke M. E. affil: Department of Clinical Neurophysiology, Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands. sug: subj: Electroencephalography Methods Signal Processing, Computer Assisted Software Algorithms Status Epilepticus Diagnosis Child, Preschool Sleep Physiology Female Male Child Child, Preschool: 2-5 years Child: 6-12 years Female Male ab: Purpose: The spike-wave index (SWI) is a key feature in the diagnosis of electrical status epilepticus during slow-wave sleep. Estimating the SWI manually is time-consuming and is subject to interrater and intrarater variability. Use of automated detection software would save time. Thereby, this software will consistently detect a certain EEG phenomenon as epileptiform and is not influenced by human factors. To determine noninferiority in calculating the SWI, we compared the performance of a commercially available spike detection algorithm (P13 software, Persyst Development Corporation, San Diego, CA) with human expert consensus.Methods: The authors identified all prolonged EEG recordings for the diagnosis or follow-up of electrical status epilepticus during slow-wave sleep carried out from January to December 2018 at an epilepsy tertiary referral center. The SWI during the first 10 minutes of sleep was estimated by consensus of two human experts. This was compared with the SWI calculated by the automated spike detection algorithm using the three available sensitivity settings: "low," "medium," and "high." In the software, these sensitivity settings are denoted as perception values.Results: Forty-eight EEG recordings from 44 individuals were analyzed. The SWIs estimated by human experts did not differ from the SWIs calculated by the automated spike detection algorithm in the "low" perception mode (P = 0.67). The SWIs calculated in the "medium" and "high" perception settings were, however, significantly higher than the human expert estimated SWIs (both P < 0.001).Conclusions: Automated spike detection (P13) is a useful tool in determining SWI, especially when using the "low" sensitivity setting. Using such automated detection tools may save time, especially when reviewing larger epochs. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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