ReviewHeliyon2023
Automatic neonatal sleep stage classification: A comparative study.
Review in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- A Lightweight Temporal-Spatial Fusion Network for Neonatal Sleep Staging.Bioengineering (Basel, Switzerland) · 2026Article
- From Cradle to Early-Life Sleep: A Bibliometric Analysis of Global Research on Neonatal Sleep.Nature and science of sleep · 2026Article
- A review of automatic sleep stage classification using machine learning algorithms based on heart rate variability.Sleep and biological rhythms · 2025Review
- A novel transformer-based approach for cardiovascular disease detection.Frontiers in digital health · 2025Article
- Near-Infrared Spectroscopy for Neonatal Sleep Classification.Sensors (Basel, Switzerland) · 2024Article
- Article
- Improved neural network with multi-task learning for Alzheimer's disease classification.Heliyon · 2024Article
- Quantitative Electroencephalography in Term Neonates During the Early Postnatal Period Across Various Sleep States.Nature and science of sleep · 2024Article
- Classification of EEG Signals Based on Sparrow Search Algorithm-Deep Belief Network for Brain-Computer Interface.Bioengineering (Basel, Switzerland) · 2023Article
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 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.
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.