Ruijie Zhang,1,* Xinran Dong,2,* Lu Zhang,1,* Xinao Lin,1 Xuefeng Wang,1 Yan Xu,3 Chuyan Wu,4 Feng Jiang,1 Jimei Wang1 

1Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, People’s Republic of China; 2Center for Molecular Medicine, Children’s Hospital of Fudan University, Shanghai, People’s Republic of China; 3Department of Neurology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, People’s Republic of China; 4Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jimei Wang; Feng Jiang, Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, People’s Republic of China, Email wjm821920@163.com; dxyjiang@163.com

Background: Neonatal sleep is pivotal for their growth and development, yet manual interpretation of raw images is time-consuming and labor-intensive. Quantitative Electroencephalography (QEEG) presents significant advantages in terms of objectivity and convenience for investigating neonatal sleep patterns. However, research on the sleep patterns of healthy neonates remains scarce. This study aims to identify QEEG markers that distinguish between different neonatal sleep cycles and analyze QEEG alterations across various sleep stages in relation to postmenstrual age.
Methods: From September 2023 to February 2024, full-term neonates admitted to the neonatology department at the Obstetrics and Gynecology Hospital of Fudan University were enrolled in this study. Electroencephalographic (EEG) recordings were obtained from neonates aged 37– 42 weeks, within 1– 7 days post-birth. The ROC curve was employed to evaluate QEEG features related to amplitude, range EEG (rEEG), spectral density, and connectivity across different sleep stages. Furthermore, regression analyses were performed to investigate the association between these QEEG characteristics and postmenstrual age.
Results: The alpha frequency band’s spectral_diff_F3 emerged as the most potent discriminator between active sleep (AS) and quiet sleep (QS). In distinguishing AS from wakefulness (W), the theta frequency’s spectral_diff_C4 was the most effective, whereas the delta frequency’s spectral_diff_P4 excelled in differentiating QS from W. During AS and QS phases, there was a notable increase in entropy within the delta frequency band across all monitored brain regions and in the spectral relative power within the theta frequency band, correlating with postmenstrual age (PMA).
Conclusion: Spectral difference showcases the highest discriminative capability across awake and various sleep states. The observed patterns of neonatal QEEG alterations in relation to PMA are consistent with the maturation of neonatal sleep, offering insights into the prediction and evaluation of brain development outcomes.

Keywords: quantitative EEG, neonates, sleep, postmenstrual age