Towards unobtrusive sleep monitoring of preterm infants

Globally, more than more than one in ten babies are born preterm, facing developmental challenges due to their immaturity. Sleep is critical for their growth, yet traditional monitoring methods in neonatal care rely on electrodes that can cause discomfort and increase infection risks. The PhD research of Dandan Zhang introduces a novel, non-invasive approach by combining cardiorespiratory signal analysis with video-based monitoring to classify sleep states in preterm infants. This method not only improves accuracy, especially in distinguishing active sleep from wakefulness, but also offers a safer, more comfortable alternative, paving the way for innovations in neonatal care and better outcomes for these vulnerable patients.

Preterm infants spend most of their time sleeping, and during this period, their autonomic nervous system and physiological functions continue to develop. Understanding how sleep patterns affect their development can provide valuable insights for caregivers, helping to identify potential issues early on and enabling timely interventions. The most important results of the research center on the validation of the feasibility of using unobtrusive methods. Dandan Zhang demonstrated that the interaction between cardiorespiratory signals—those related to the heart and respiratory systems—can effectively distinguish between different sleep states (active sleep, quiet sleep, and wakefulness) in preterm infants. Additionally, the integration of video-based monitoring with cardiorespiratory data significantly improved the accuracy of sleep state classification. This novel approach was particularly useful in addressing the challenge of distinguishing between active sleep and wakefulness, which was difficult using only cardiorespiratory signals.

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