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...

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Published in	Journal of clinical monitoring and computing Vol. 32; no. 6; pp. 977 - 992
Main Authors	Asgari, Shadnaz, Moshirvaziri, Hana, Scalzo, Fabien, Ramezan-Arab, Nima
Format	Journal Article
Language	English
Published	Dordrecht Springer Netherlands 01.12.2018 Springer Nature B.V
Subjects	Anesthesiology Cardiac arrest Critical Care Medicine Electroencephalography Health Sciences Hypothermia Intensive Literature reviews Medicine Medicine & Public Health Pattern recognition Prognosis Resuscitation Review Paper State-of-the-art reviews Statistics for Life Sciences Test procedures Therapeutic hypothermia Prognostication Electroencephalogram Quantitative EEG markers Cardiac arrest
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ISSN	1387-1307 1573-2614 1573-2614
DOI	10.1007/s10877-018-0118-3

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Abstract	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.
AbstractList	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. 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.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. 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.
Author	Scalzo, Fabien Asgari, Shadnaz Moshirvaziri, Hana Ramezan-Arab, Nima
Author_xml	– sequence: 1 givenname: Shadnaz surname: Asgari fullname: Asgari, Shadnaz email: Shadnaz.Asgari@csulb.edu organization: Biomedical Engineering Department, California State University, Long Beach, Computer Engineering and Computer Science Department, California State University – sequence: 2 givenname: Hana surname: Moshirvaziri fullname: Moshirvaziri, Hana organization: Biomedical Engineering Department, California State University, Long Beach – sequence: 3 givenname: Fabien surname: Scalzo fullname: Scalzo, Fabien organization: Department of Computer Science, University of California, Department of Neurology, University of California – sequence: 4 givenname: Nima surname: Ramezan-Arab fullname: Ramezan-Arab, Nima organization: Department of Neurology, University of California, Neuroscience and Stroke program, Long Beach Memorial Medical Center
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Snippet	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... 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...
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SubjectTerms	Anesthesiology Cardiac arrest Critical Care Medicine Electroencephalography Health Sciences Hypothermia Intensive Literature reviews Medicine Medicine & Public Health Pattern recognition Prognosis Resuscitation Review Paper State-of-the-art reviews Statistics for Life Sciences Test procedures
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Title	Quantitative measures of EEG for prediction of outcome in cardiac arrest subjects treated with hypothermia: a literature review
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