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On the development of sleep states in the first weeks of life
Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes correspond to the rapid cortical development at this age. Manual sleep classification is specifically challenging in this population give...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6818777/ https://www.ncbi.nlm.nih.gov/pubmed/31661522 http://dx.doi.org/10.1371/journal.pone.0224521 |
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author | Wielek, Tomasz Del Giudice, Renata Lang, Adelheid Wislowska, Malgorzata Ott, Peter Schabus, Manuel |
author_facet | Wielek, Tomasz Del Giudice, Renata Lang, Adelheid Wislowska, Malgorzata Ott, Peter Schabus, Manuel |
author_sort | Wielek, Tomasz |
collection | PubMed |
description | Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes correspond to the rapid cortical development at this age. Manual sleep classification is specifically challenging in this population given major body movements and frequent shifts between vigilance states; in addition various staging criteria co-exist. In the present study we utilized a machine learning approach and investigated how EEG complexity and sleep stages evolve during the very first weeks of life. We analyzed 42 full-term infants which were recorded twice (at week two and five after birth) with full polysomnography. For sleep classification EEG signal complexity was estimated using multi-scale permutation entropy and fed into a machine learning classifier. Interestingly the baby’s brain signal complexity (and spectral power) revealed developmental changes in sleep in the first 5 weeks of life, and were restricted to NREM (“quiet”) and REM (“active sleep”) states with little to no changes in state wake. Data demonstrate that our classifier performs well over chance (i.e., >33% for 3-class classification) and reaches almost human scoring accuracy (60% at week-2, 73% at week-5). Altogether, these results demonstrate that characteristics of newborn sleep develop rapidly in the first weeks of life and can be efficiently identified by means of machine learning techniques. |
format | Online Article Text |
id | pubmed-6818777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-68187772019-11-01 On the development of sleep states in the first weeks of life Wielek, Tomasz Del Giudice, Renata Lang, Adelheid Wislowska, Malgorzata Ott, Peter Schabus, Manuel PLoS One Research Article Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes correspond to the rapid cortical development at this age. Manual sleep classification is specifically challenging in this population given major body movements and frequent shifts between vigilance states; in addition various staging criteria co-exist. In the present study we utilized a machine learning approach and investigated how EEG complexity and sleep stages evolve during the very first weeks of life. We analyzed 42 full-term infants which were recorded twice (at week two and five after birth) with full polysomnography. For sleep classification EEG signal complexity was estimated using multi-scale permutation entropy and fed into a machine learning classifier. Interestingly the baby’s brain signal complexity (and spectral power) revealed developmental changes in sleep in the first 5 weeks of life, and were restricted to NREM (“quiet”) and REM (“active sleep”) states with little to no changes in state wake. Data demonstrate that our classifier performs well over chance (i.e., >33% for 3-class classification) and reaches almost human scoring accuracy (60% at week-2, 73% at week-5). Altogether, these results demonstrate that characteristics of newborn sleep develop rapidly in the first weeks of life and can be efficiently identified by means of machine learning techniques. Public Library of Science 2019-10-29 /pmc/articles/PMC6818777/ /pubmed/31661522 http://dx.doi.org/10.1371/journal.pone.0224521 Text en © 2019 Wielek et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wielek, Tomasz Del Giudice, Renata Lang, Adelheid Wislowska, Malgorzata Ott, Peter Schabus, Manuel On the development of sleep states in the first weeks of life |
title | On the development of sleep states in the first weeks of life |
title_full | On the development of sleep states in the first weeks of life |
title_fullStr | On the development of sleep states in the first weeks of life |
title_full_unstemmed | On the development of sleep states in the first weeks of life |
title_short | On the development of sleep states in the first weeks of life |
title_sort | on the development of sleep states in the first weeks of life |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6818777/ https://www.ncbi.nlm.nih.gov/pubmed/31661522 http://dx.doi.org/10.1371/journal.pone.0224521 |
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