On the development of sleep states in the first weeks of life


Autoři: Tomasz Wielek aff001;  Renata Del Giudice aff003;  Adelheid Lang aff001;  Malgorzata Wislowska aff001;  Peter Ott aff004;  Manuel Schabus aff001
Působiště autorů: Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria aff001;  Centre for Cognitive Neuroscience (CCNS), University of Salzburg, Salzburg, Austria aff002;  Department of Health Sciences, Università degli Studi di Milano, Milan, Italy aff003;  ITS Informationstechnik & System-Management, Salzburg University of Applied Sciences, Salzburg, Austria aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224521

Souhrn

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.

Klíčová slova:

Age groups – Electrocardiography – Electroencephalography – Entropy – Machine learning – Neonates – Permutation – Sleep


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