Abstract
Established sleep cycling is one of the main hallmarks of early brain development in preterm infants, therefore, automated classification of the sleep stages in preterm infants can be used to assess the neonate’s cerebral maturation. Tensor algebra is a powerful tool to analyze multidimensional data and has proven successful in many applications. In this paper, a novel unsupervised algorithm to identify neonatal sleep stages based on the decomposition of a multiscale entropy tensor is presented. The method relies on the difference in electroencephalography(EEG) complexity between the neonatal sleep stages and is evaluated on a dataset of 97 EEG recordings. An average sensitivity, specificity, accuracy and area under the receiver operating characteristic curve of 0.80, 0.79, 0.79 and 0.87 was obtained if the rank of the tensor decomposition is selected based on the age of the infant.
Highlights
IntroductionThe emergence of sleep cycles occurs at approximately 26 to 28 weeks postmenstrual age (PMA) [1]
In human infants, the emergence of sleep cycles occurs at approximately 26 to 28 weeks postmenstrual age (PMA) [1]
This paper proposes a novel unsupervised method to discriminate quiet sleep from non-quiet sleep in preterm infants
Summary
The emergence of sleep cycles occurs at approximately 26 to 28 weeks postmenstrual age (PMA) [1]. The proportion of time spent asleep decreases, while the relative amount of quiet sleep increases Near term age, both active sleep and quiet sleep constitute approximately half of the total sleep time [2,3]. Sleep and established sleep cycling play a vital role in normal neurosensory development, learning processes, memory consolidation and in the protection of the infant’s brain plasticity [1]. Studies such as that conducted by Shellhaas et al [5] have shown that the presence of sleep cycling and the quantity and quality of each sleep state are associated with neurodevelopmental outcomes [6,7,8]
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