Abstract
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 huge 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.
Author summary The organization of newborn sleep differs from adult sleep, and its ongoing maturation over time corresponds with cortical development. However, sleep scoring in this population is challenging given frequent artifacts in polysomnography (PSG) and absence of established staging criteria which results in low inter-scorer reliability. To investigate changes in the early brain activity, we analyzed large sample of newborn data at week 2 and 5 after birth. First we evaluated sleep that was previously scored visually, in terms of both entropy and oscillatory power. Next we accessed the performance of automatic sleep scoring based on machine learning compared with conventional, manual scoring. We observed clear developmental changes on the brain-level in the first 5 weeks of life in human newborns. These changes were limited to “quiet” NREM and “active” REM sleep. Also our classifier data demonstrated that we can classify well above chance and similar to human scorers using multi-scale permutation entropy (and just 6 EEG and 5 physiological channels).
