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Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques
Hemodynamic changes during neonatal transition increase the vulnerability of the preterm brain to injury. Real-time monitoring of brain function during this period would help identify the immediate impact of these changes on the brain. Neonatal EEG provides detailed real-time information about newbo...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509809/ https://www.ncbi.nlm.nih.gov/pubmed/31131267 http://dx.doi.org/10.3389/fped.2019.00174 |
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author | O'Toole, John M. Boylan, Geraldine B. |
author_facet | O'Toole, John M. Boylan, Geraldine B. |
author_sort | O'Toole, John M. |
collection | PubMed |
description | Hemodynamic changes during neonatal transition increase the vulnerability of the preterm brain to injury. Real-time monitoring of brain function during this period would help identify the immediate impact of these changes on the brain. Neonatal EEG provides detailed real-time information about newborn brain function but can be difficult to interpret for non-experts; preterm neonatal EEG poses even greater challenges. An objective quantitative measure of preterm brain health would be invaluable during neonatal transition to help guide supportive care and ultimately protect the brain. Appropriate quantitative measures of preterm EEG must be calculated and care needs to be taken when applying the many techniques available for this task in the era of modern data science. This review provides valuable information about the factors that influence quantitative EEG analysis and describes the common pitfalls. Careful feature selection is required and attention must be paid to behavioral state given the variations encountered in newborn EEG during different states. Finally, the detrimental influence of artifacts on quantitative EEG analysis is illustrated. |
format | Online Article Text |
id | pubmed-6509809 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65098092019-05-24 Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques O'Toole, John M. Boylan, Geraldine B. Front Pediatr Pediatrics Hemodynamic changes during neonatal transition increase the vulnerability of the preterm brain to injury. Real-time monitoring of brain function during this period would help identify the immediate impact of these changes on the brain. Neonatal EEG provides detailed real-time information about newborn brain function but can be difficult to interpret for non-experts; preterm neonatal EEG poses even greater challenges. An objective quantitative measure of preterm brain health would be invaluable during neonatal transition to help guide supportive care and ultimately protect the brain. Appropriate quantitative measures of preterm EEG must be calculated and care needs to be taken when applying the many techniques available for this task in the era of modern data science. This review provides valuable information about the factors that influence quantitative EEG analysis and describes the common pitfalls. Careful feature selection is required and attention must be paid to behavioral state given the variations encountered in newborn EEG during different states. Finally, the detrimental influence of artifacts on quantitative EEG analysis is illustrated. Frontiers Media S.A. 2019-05-03 /pmc/articles/PMC6509809/ /pubmed/31131267 http://dx.doi.org/10.3389/fped.2019.00174 Text en Copyright © 2019 O'Toole and Boylan. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pediatrics O'Toole, John M. Boylan, Geraldine B. Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques |
title | Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques |
title_full | Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques |
title_fullStr | Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques |
title_full_unstemmed | Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques |
title_short | Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques |
title_sort | quantitative preterm eeg analysis: the need for caution in using modern data science techniques |
topic | Pediatrics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509809/ https://www.ncbi.nlm.nih.gov/pubmed/31131267 http://dx.doi.org/10.3389/fped.2019.00174 |
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