Abstract
Objective. We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38–40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification. Approach. EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording’s feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen’s kappa agreement calculated between the estimates and clinicians’ visual labels. Main results. For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively. Significance. This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may lead to learning problems by school age.
Highlights
In the Neonatal Intensive Care Unit (NICU), both term-born and preterm infants are treated
For two-state classification, a mean of 7.3 (±2.8) features across the 8 training folds were selected for the Gaussian mixture models (GMMs), and a mean of 8.8 (±1.3) features for the Hidden Markov models (HMMs)
We identified the most prominent features, as selected by minimum redundancy maximum relevance (mRMR), by determining an overall weighted ranking from the individual feature ranks across the training folds
Summary
In the Neonatal Intensive Care Unit (NICU), both term-born and preterm infants are treated. Preterm infants are born at 37 weeks) are admitted because of congenital anomalies or acute perinatal disease. Both cohorts of babies spend a large proportion of their early life in sleep, whereby the brain continues to build complex cortical pathways leading to memory and enhanced cognition [5, 6]. Better understanding of the precise nature and transitions of the different sleep states can help identify potential developmental delays early, which result from these various cognitive stresses [7,8,9,10,11]
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