Quantitative measures of EEG for prediction of outcome in cardiac arrest subjects treated with hypothermia: a literature review.
Cardiac arrest (CA) is the leading cause of death and disability in the United States. Early and accurate prediction of CA outcome can help clinicians and families to make a better-informed decision for the patient's healthcare. Studies have shown that electroencephalography (EEG) may assist in earl...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 32; no. 6; pp. 977 - 993 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Dec2018
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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=132699969&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132699969 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Dec2018 vid: 32 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132699969 132699969 NLM29480385 132699969 10.1007/s10877-018-0118-3 NLM29480385 132699969 ppf: 977 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Quantitative measures of EEG for prediction of outcome in cardiac arrest subjects treated with hypothermia: a literature review. aug: au: Asgari, Shadnaz Moshirvaziri, Hana Scalzo, Fabien Ramezan-Arab, Nima affil: Biomedical Engineering Department, California State University, Long Beach, 1250 Bellflower Blvd.-MS 8302, 90840-8302, Long Beach, CA, USA sug: subj: Electroencephalography Statistics and Numerical Data Heart Arrest Therapy Hypothermia, Induced Signal Processing, Computer Assisted Human Prognosis Statistics Brain Physiopathology Recovery Treatment Outcomes Heart Arrest Physiopathology Funding Source ab: Cardiac arrest (CA) is the leading cause of death and disability in the United States. Early and accurate prediction of CA outcome can help clinicians and families to make a better-informed decision for the patient's healthcare. Studies have shown that electroencephalography (EEG) may assist in early prognosis of CA outcome. However, visual EEG interpretation is subjective, labor-intensive, and requires interpretation by a medical expert, i.e., neurophysiologists. These limiting factors may hinder the applicability of such testing as the prognostic method in clinical settings. Automatic EEG pattern recognition using quantitative measures can make the EEG analysis more objective and less time consuming. It also allows to detect and display hidden patterns that may be useful for the prognosis over longer time periods of monitoring. Given these potential benefits, there have been an increasing interest over the last few years in the development and employment of EEG quantitative measures to predict CA outcome. This paper extensively reviews the definition and efficacy of various measures that have been employed for the prediction of outcome in CA subjects undergoing hypothermia (a neuroprotection method that has become a standard of care to improve the functional recovery of CA patients after resuscitation). The review details the State-of-the-Art and provides some perspectives on what seems to be promising for the early and accurate prognostication of CA outcome using the quantitative measures of EEG. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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