ArticleInternational journal of neural systems2017
An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.
Article in International journal of neural systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03493113 (Transient Hypothyroxinemia of Prematurity), which is not on this map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Transient Hypothyroxinemia of Prematurity: Electrophysiological Changes in the Preterm Infants' Brain, a Retrospective Study
Who cites it
20 citing papers in PubMed, 67 citations in OpenAlex.
- A robust deep learning framework for automated fetal behavioral state classification: leveraging multi-center datasets to improve antepartum heart rate monitoring.Biomedical signal processing and control · 2026Article
- EEG electrode setup optimization using feature extraction techniques for neonatal sleep state classification.Frontiers in computational neuroscience · 2025Article
- Near-Infrared Spectroscopy for Neonatal Sleep Classification.Sensors (Basel, Switzerland) · 2024Article
- Microstate Analysis Reflects Maturation of the Preterm Brain.Brain topography · 2024Article
- Sleep state organisation of moderate to late preterm infants in the neonatal unit.Pediatric research · 2023Observational
- Article
- Evaluation of parameters for fetal behavioural state classification.Scientific reports · 2022Article
- A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- Article
- Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals.Sensors (Basel, Switzerland) · 2021Article
- Culture and the organization of infant sleep: A study in the Netherlands and the U.S.A.Infant behavior & development · 2021Article
- Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care.Frontiers in pediatrics · 2021Review
- Identifying tracé alternant activity in neonatal EEG using an inter-burst detection approach.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Neonatal sleep stage identification using long short-term memory learning system.Medical & biological engineering & computing · 2020Article
- Maturation of the Autonomic Nervous System in Premature Infants: Estimating Development Based on Heart-Rate Variability Analysis.Frontiers in physiology · 2020Article
- An Open Source Classifier for Bed Mattress Signal in Infant Sleep Monitoring.Frontiers in neuroscience · 2020Article
- Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation.Frontiers in pediatrics · 2020Article
- A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals.International journal of environmental research and public health · 2019Article
- Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques.Frontiers in pediatrics · 2019Review
- Review of sleep-EEG in preterm and term neonates.Early human development · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
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 ([Formula: see text] 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 [Formula: see text]), using Sensitivity, Specificity, Detection Factor ([Formula: see text] of visual QS periods correctly detected by CLASS) and Misclassification Factor ([Formula: see text] of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31-38 weeks (median [Formula: see text], 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 [Formula: see text]). 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.