An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation

Abstract

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age (PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27-42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0.93), using Sensitivity, Specificity, Detection Factor (DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor (MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31-38 weeks (median DF = 1.0, median MF 0-0.25, median Sensitivity 0.93-1.0, and median Specificity 0.80-0.91 across this age range), with minimal misclassifications at 35-36 weeks (median MF = 0). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

Review of sleep-EEG in preterm and term neonates

“…Stefanski et al [36] found an overall agreement of 87% for scoring EEG sleep patterns, whereas the agreement was 10% lower for coding behavioural patterns, and decreased with lower GA. In our own cohort of preterm infants (PMA 27–41 weeks), we found a Cohen's Kappa of 0.93 (95% CI: 0.90–0.95) for QS versus non-QS classified epochs, with the lowest Kappa value for 27–31 weeks PMA [37]. Nevertheless, scoring all different sleep states will be more challenging and induce higher disagreement when characterizing immature sleep patterns, since strict criteria for scoring sleep in preterm infants are lacking [38].…”

“…8). This leads to a globally more continuous EEG and the relative change in discontinuity between QS and non-QS states is only minor [37,47]. A regular respiratory rate and no REMs are the most consistent non-cerebral features associated with QS.…”

“…In our own cohort with PMA between 27 and 31 weeks, we found a slightly shorter cycle duration between two successive QS periods based on quantitative analysis (for detailed description of this study group see [37]). This is comparable to results of visual EEG-assessment of Curzi Dascalavoa et al, and coherent with the observation of a shorter cycle duration reported by Vecchierini et al [44].…”

Complexity Analysis of Neonatal EEG Using Multiscale Entropy: Applications in Brain Maturation and Sleep Stage Classification

“…After quantifying the multiscale entropy of each EEG channel, four features were extracted from the multiscale entropy curve: (1) the area under the multiscale curve (this will be referred to as the complexity index); (2) the average slope of the multiscale entropy curve in the small scales (scale 1-5); (3) the average slope of the curve in the large scales (scale [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]; and (4) the maximum value of the multiscale entropy curve. Thus, in total, a set of 32 (8 channels × 4) features are extracted.…”

Monitoring Effective Connectivity in the Preterm Brain: A Graph Approach to Study Maturation

“…The second dataset comprised 25 preterm infants, with gestational age (GA) ≤ 32 weeks, who were recruited for a larger EEG study to assess brain development and automatically detect quiet sleep epochs [20,21]. Each patient was enrolled with informed parental consent at the Neonatal Intensive Care Unit (NICU) of the University Hospital of Leuven, Belgium.…”