Neonatal EEG Sleep Stages Modelling by Temporal Profiles

Krajča, Vladimı́r; Petránek, Svojmil; Mohylova, Jitka; Paul, K; Gerla, Václav; Lhotská, Lenka

doi:10.1007/978-3-540-75867-9_25

Cited by 13 publications

(13 citation statements)

References 8 publications

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“…Moreover, multiple sleep-EEG studies indicate that newborn state recognition is more comprehensively assessed when integrating both cerebral and non-cerebral measures. Previous attempts were either based on single channel EEG in very preterm infants < 32 weeks [49,72,73] or focused on the neonatal, term EEG [62,74–76] or mainly determined quantitative features based on visual pre-selection of sleep states [5,70,77–80]. Quantitative EEG-features that are most related with brain maturation and applied to sleep in previous studies, are spectral analysis (which decomposes the EEG signal into its constituent frequency components) [23,79,81] and methods to define EEG discontinuity [4,49,82,83].…”

Section: Automated Sleep-eeg Analysismentioning

confidence: 99%

Review of sleep-EEG in preterm and term neonates

Dereymaeker

Pillay

Vervisch

et al. 2017

Early Human Development

108

117

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Neonatal sleep is a crucial state that involves endogenous driven brain activity, important for neuronal survival and guidance of brain networks. Sequential EEG-sleep analysis in preterm infants provides insights into functional brain integrity and can document deviations of the biologically pre-programmed process of sleep ontogenesis during the neonatal period. Visual assessment of neonatal sleep-EEG, with integration of both cerebral and non-cerebral measures to better define neonatal state, is still considered the gold standard. Electrographic patterns evolve over time and are gradually time locked with behavioural characteristics which allow classification of quiet sleep and active sleep periods during the last 10weeks of gestation. Near term age, the neonate expresses a short ultradian sleep cycle, with two distinct active and quiet sleep, as well as brief periods of transitional or indeterminate sleep. Qualitative assessment of neonatal sleep is however challenged by biological and environmental variables that influence the expression of EEG-sleep patterns and sleep organization. Developing normative EEG-sleep data with the aid of automated analytic methods, can further improve our understanding of extra-uterine brain development and state organization under stressful or pathological conditions. Based on those developmental biomarkers of normal and abnormal brain function, research can be conducted to support and optimise sleep in the NICU, with the ultimate goal to improve therapeutic interventions and neurodevelopmental outcome.

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Section: Automated Sleep-eeg Analysismentioning

confidence: 99%

Review of sleep-EEG in preterm and term neonates

Dereymaeker

Pillay

Vervisch

et al. 2017

Early Human Development

108

117

View full text Add to dashboard Cite

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“…Krajca et al proposed a method that involved segmenting the EEG periods and extracting simple time-domain and frequency-domain features which were then clustered into distinct groups. The evolution of these cluster labels over time reflected transitions into and out of QS 25–27. However, the method was vulnerable to high power artifacts and the concept was illustrated only on a single recording at term age.…”

Section: Introductionmentioning

confidence: 99%

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

Dereymaeker

Pillay

Vervisch

et al. 2017

Int. J. Neur. Syst.

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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.

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“…In extended wakefulness, there is a negative correlation with α power and positive correlation with θ power [47]. In this sense, the brain activity could be categorized by interpreting the signal power, in theory [48].…”

Section: Materials and Methodologymentioning

confidence: 99%

Investigation of New Statistical Features for BCI Based Sleep Stages Recognition through EEG Bio-signals

Sefik

Elibol

Ince³

et al. 2014

Intelligent Computing in Bioinformatics

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Abstract. Electroencephalogram (EEG) is one of the oldest techniques available to read brain data. It is a methodology to measure and record the electrical activity of brain using sensitive sensors attached to the scalp. Brain's electrical activity is visualized on computers in form of signals through BCI tools. It is also possible to convert these signals into digital commands to provide human-computer interaction (HCI) through adaptive user interfaces. In this study, a set of statistical features: mean entropy, skew-ness, kurtosis and mean power of wavelets are proposed to enhance human sleep stages recognition through EEG signal. Additionally, an adaptive user interface for vigilance level recognition is introduced. One-way ANOVA test is employed for feature selection. EEG signals are decomposed into frequency sub-bands using discrete wavelet transform and selected statistical features are employed in SVM for recognition of human sleep stages: stage 1, stage 3, stage REM and stage AWAKE. According to experimental results, proposed statistical features have a significant discrimination rate for true classification of sleep stages with linear SVM. The accuracy of linear SVM reaches to 93% in stage 1, 82% in stage 3, 73% in stage REM and 96% in stage AWAKE with proposed statistical features.

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Neonatal EEG Sleep Stages Modelling by Temporal Profiles

Cited by 13 publications

References 8 publications

Review of sleep-EEG in preterm and term neonates

Review of sleep-EEG in preterm and term neonates

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

Investigation of New Statistical Features for BCI Based Sleep Stages Recognition through EEG Bio-signals

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