ArticleSensors (Basel, Switzerland)2022
Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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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.
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Who cites it
41 citing papers in PubMed, 104 citations in OpenAlex.
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- Sleep Disturbances Across Dementias and Cognitive Decline: Study Protocol for a Systematic Review and Network Meta-Analysis of Polysomnographic Findings.Journal of clinical medicine · 2025Article
- Forehead and In-Ear EEG Acquisition and Processing: Biomarker Analysis and Memory-Efficient Deep Learning Algorithm for Sleep Staging with Optimized Feature Dimensionality.Sensors (Basel, Switzerland) · 2025Article
- Electroencephalography-based evaluation of the impact of night-time nursing care on sleep during intensive care unit administration after cardiac surgery.Scientific reports · 2025Article
- Exploring the Potential of Electroencephalography Signal-Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis.JMIR medical informatics · 2025Article
- Machine learning classifier solving the problem of sleep stage imbalance between overnight sleep.Biomedical engineering letters · 2025Article
- Variability and predictability in human sleep.Brain communications · 2025Article
- The Improvement in Sleep Quality by Zizyphi Semen in Rodent Models Through GABAergic Transmission Regulation.Nutrients · 2024Article
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- Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial.Frontiers in psychiatry · 2024Article
- A Film Electrode upon Nanoarchitectonics of Bacterial Cellulose and Conductive Fabric for Forehead Electroencephalogram Measurement.Sensors (Basel, Switzerland) · 2023Article
- Sleep-Enhancing Effect of Water Extract from Jujube (Foods (Basel, Switzerland) · 2023Article
- Design of a Sleep Modulation System with FPGA-Accelerated Deep Learning for Closed-loop Stage-Specific In-Phase Auditory Stimulation.IEEE International Symposium on Circuits and Systems proceedings. IEEE International Symposium on Circuits and Systems · 2023Article
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- Recent Progress in Long-Term Sleep Monitoring Technology.Biosensors · 2023Review
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system.
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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.