Automated sleep scoring: A review of the latest approaches

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Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review

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An Ensemble Voting Approach With Innovative Multi-Domain Feature Fusion for Neonatal Sleep Stratification

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Automatic Sleep Scoring Using Intrinsic Mode Based on Interpretable Deep Neural Networks

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Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy

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Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

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Landscape of biomedical informatics standards and terminologies for clinical sleep medicine research: A systematic review

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Depression Detection Using an Automatic Sleep Staging Method With an Interpretable Channel-Temporal Attention Mechanism

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Changes in the Term Neonatal Electroencephalogram with General Anesthesia: A Systematic Review with Narrative Synthesis

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Effects of commonly used analgesics on sleep architecture: a topical review

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Retrospective validation of automatic sleep analysis with grey areas model for human‐in‐the‐loop scoring approach

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Performance evaluation of the open-source Yet Another Spindle Algorithm sleep staging algorithm against gold standard manual evaluation of polysomnographic records in adolescence

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End-to end decision support system for sleep apnea detection and Apnea-Hypopnea Index calculation using hybrid feature vector and Machine learning

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MAGSleepNet: Adaptively multi-scale temporal focused sleep staging model for multi-age groups

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Towards a user-friendly sleep staging system for polysomnography part I: Automatic classification based on medical knowledge

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Combining transient statistical markers from the EEG signal to predict brain sensitivity to general anesthesia

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Automated sleep staging algorithms: have we reached the performance limit due to manual scoring?

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From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People

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Generalized sleep decoding with basal ganglia signals in multiple movement disorders

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A Lightweight Model for Physiological Signals-Based Sleep Staging With Multiclass CAM for Model Explainability

Yang, Y., & Li, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3430009 · Cited by 9

Enhancing automatic sleep stage classification with cerebellar EEG and machine learning techniques

Wang, M., Guan, J., Tong, S., Wang, J., Yikai, Y., Yicheng, Z., Yi, Z., Xiaoyu, Y., Li, X., Yang, J., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109515 · Cited by 9

Identifying Elevated Risk for Future Pain Crises in Sickle-Cell Disease Using Photoplethysmogram Patterns Measured During Sleep: A Machine Learning Approach

Ji, Y., Chalacheva, P., ROSEN, C. L., DeBaun, M. R., Coates, T. D., & Khoo, M. C. K. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.714741 · Cited by 9

Feasibility of wearable devices and machine learning for sleep classification in children with Rett syndrome: A pilot study

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Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal

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Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring

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Channel Contribution in Deep Learning Based Automatic Sleep Scoring—How Many Channels Do We Need?

Lu, C., Pathak, S., Englebienne, G., & Seifert, C. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3227040 · Cited by 9

Challenges of Applying Automated Polysomnography Scoring at Scale

Álvarez-Estévez, D. (2023) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2023.05.002 · Cited by 8

Analysing the impact of body position shift on sleep architecture and stage transition: A comprehensive multidimensional study using event‐synchronised polysomnography data

Ahn, H. K., Kang, Y. J., Yoon, W., & Shin, H. (2023) · Journal of Sleep Research · doi:10.1111/jsr.14115 · Cited by 9

Sleep Research in the Era of AI

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A method based on cardiopulmonary coupling analysis for sleep quality assessment with FPGA implementation

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Snoozing: an examination of a common method of waking

Mattingly, S. M., Martinez, G. J., Young, J., Cain, M. K., & Striegel, A. (2022) · SLEEP · doi:10.1093/sleep/zsac184 · Cited by 8

The Future of Sleep Staging, Revisited

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A federated semi-supervised automatic sleep staging method based on relationship knowledge sharing

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Automated sleep staging on reduced channels in children with epilepsy

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Self-Supervised Transformer Model Training for a Sleep-EEG Foundation Model

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The Boston Children’s Hospital sleep corpus: a collection of 15695 annotated pediatric polysomnograms

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Comparison analysis between standard polysomnographic data and in-ear-electroencephalography signals: a preliminary study

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External proficiency testing improves inter-scorer reliability of polysomnography scoring

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Automatic Sleep Stage Classification with Optimized Selection of EEG Channels

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Médecine du sommeil personnalisée et syndrome d’apnées hypopnées obstructives du sommeil : entre précision et stratification, une proposition de clarification

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MFSleepNet: A multi-receptive field sleep networks for sleep stage classification

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Emerging challenges in the transition from manual to automated sleep scoring

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CrossFusionSleepNet: A multimodal deep learning model for automatic sleep stage classification

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Multidomain Selective Feature Fusion and Stacking Based Ensemble Framework for EEG-Based Neonatal Sleep Stratification

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An inter-rater variability study between human and automatic scorers in 5-s mini-epochs of sleep

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Hypnogram and Hypnodensity Analysis of REM Sleep Behaviour Disorder Using Both EEG and HRV ‐Based Sleep Staging Models

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Evaluation of Automated Hypnogram Analysis on Multi-Scored Polysomnographies

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Decentralized Data-Privacy Preserving Deep-Learning Approaches for Enhancing Inter-Database Generalization in Automatic Sleep Staging

Anido-Alonso, A., & Álvarez-Estévez, D. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3310869 · Cited by 6

Automatic Sleep Stage Detection using a Single Channel Frontal EEG

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Manual Annotation of Time in Bed Using Free-Living Recordings of Accelerometry Data

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Three simple steps to improve the interpretability of EEG-SVM studies

Joucla, C., Gabriel, D., Ortega, J., & Haffen, É. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00221.2022 · Cited by 7

An explainable deep learning approach for sleep staging in sleep apnea patients across all age subgroups from pulse oximetry signals

Vaquerizo-Villar, F., Gutiérrez‐Tobal, G. C., Álvarez, D., Martín‐Montero, A., Gozal, D., & Hornero, R. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112562 · Cited by 5

A Novel NICU Sleep State Stratification: Multiperspective Features, Adaptive Feature Selection and Ensemble Model

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SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models

Rossi, A. D., Metaldi, M., Bechný, M., Filchenko, I., van der Meer, J., Schmidt, M. H., Bassetti, C. L., Tzovara, A., Faraci, F. D., & Fiorillo, L. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02237-2 · Cited by 5

A machine learning eye movement detection algorithm using electrooculography

Dupre, A., Cronin, M. F. M., Schmugge, S. J., Tate, S., Wack, A., Prescott, B. R., Li, C., Auerbach, S. H., Suchdev, K., Al‐Faraj, A. O., et al. (2022) · SLEEP · doi:10.1093/sleep/zsac254 · Cited by 5

4s-SleepGCN: Four-Stream Graph Convolutional Networks for Sleep Stage Classification

Li, M., Chen, H., Liu, Y., & Zhao, Q. (2023) · IEEE Access · doi:10.1109/access.2023.3294410 · Cited by 6

Comparison of EEG and EOG signals in classification of sleep stages

Manshouri, N. (2023) · Pamukkale University Journal of Engineering Sciences · doi:10.5505/pajes.2022.88122 · Cited by 5

Exploiting Spatial-Temporal Data for Sleep Stage Classification via Hypergraph Learning

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The effects of slow wave sleep characteristics on semantic, episodic, and procedural memory in people with epilepsy

Höller, Y., Eyjólfsdóttir, S. G., van Schalkwijk, F. J., & Trinka, E. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1374760 · Cited by 6

Mid-life sleep is associated with cognitive performance later in life in aging American Indians: data from the Strong Heart Study

Fonseca, L. M., Finlay, M., Chaytor, N. S., Morimoto, N. G., Buchwald, D., Dongen, H. P. A. V., Quan, S. F., & Suchy‐Dicey, A. M. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1346807 · Cited by 5

Decrease in wearable-based nocturnal sleep efficiency precedes epileptic seizures

Gagliano, L., Ding, T. Y., Toffa, D. H., Beauregard, L., Robert, M., Lesage, F., Sawan, M., Nguyen, D. K., & Assi, E. B. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2022.1089094 · Cited by 5

Towards a Deeper Understanding of Sleep Stages through their Representation in the Latent Space of Variational Autoencoders

Biedebach, L., Rusanen, M., Leppänen, T., Islind, A. S., Thordarson, B., Arnardottir, E., Óskarsdóttir, M., Korkalainen, H., Nikkonen, S., Kainulainen, S., et al. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.382 · Cited by 5

Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study

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Beyond traditional visual sleep scoring: massive feature extraction and unsupervised clustering of sleep time seriesPreprint

Decat, N., Walter, J., Koh, Z. H., Sribanditmongkol, P., Fulcher, B., Windt, J., Andrillon, T., & Tsuchiya, N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.08.458981 · Cited by 6

Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep ScoringPreprint

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SoftVotingSleepNet: Majority Vote of Deep Learning Models for Sleep Stage Classification from Raw Single EEG Channel

Neshov, N. N., Tonchev, K., Velchev, Y. S., Manolova, A. H., & Poulkov, V. K. (2022) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom54372.2022.9858199 · Cited by 4

Tree-Based Machine Learning Techniques for Automated Human Sleep Stage Classification

Arslan, R. S., Ulutaş, H., Köksal, A. S., Bakır, M., & Çiftçi, B. (2023) · Traitement du signal · doi:10.18280/ts.400408 · Cited by 5

Sleep quality and comfort in fully automated vehicles: A comparison of two seat configurations

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Shorter duration of slow wave sleep is related to symptoms of depression in patients with epilepsy

Eyjólfsdóttir, S. G., Trinka, E., & Höller, Y. (2023) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2023.109515 · Cited by 4

MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice

Jiang, C., Xie, W., Zheng, J., Yan, B., Luo, J., & Zhang, J. (2024) · Biosensors · doi:10.3390/bios14080406 · Cited by 5

Deep learning-assisted non-invasive pediatric tic disorder diagnosis using EEG features extracted by residual neural networks

Wang, C., Tan, X., Zhu, B., Zhao, Z., Wang, Q., Yang, Y., Liu, J., Fu, C., Wang, J., & Lin, Y. (2024) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2024.101151 · Cited by 4

SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data

Falach, R., Belonosov, G., Schmidig, F. J., Aderka, M., Zhelezniakov, V., Shani‐Hershkovich, R., Bar, E., & Nir, Y. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110232 · Cited by 9

Error Propagation from Sleep Stage Classification to Derived Sleep Parameters in Machine Learning on Data from Wearables

Hardarson, E., Íslind, A. S., Arnardóttir, E. S., & Óskarsdóttir, M. (2023) · Current Sleep Medicine Reports · doi:10.1007/s40675-023-00253-w · Cited by 4

An algorithmic approach to identification of gray areas: Analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model

Jouan, G., Arnardóttir, E. S., Íslind, A. S., & Óskarsdóttir, M. (2023) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.09.039 · Cited by 5

Automated Sleep Stage Scoring Using k-Nearest Neighbors Classifier

Kiss, T., Morairty, S. R., Schwartz, M., Kilduff, T. S., Buhl, D. L., & Volfson, D. (2020) · The Journal of Open Source Software · doi:10.21105/joss.02377 · Cited by 4

Automated Sleep Staging System Based on Ensemble Learning Model Using Single-Channel EEG Signal

Satapathy, S. K., Kondaveeti, H. K., & Malladi, R. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82469-3_17 · Cited by 4

A journey toward artificial intelligence-assisted automated sleep scoring

Chang, R. B. (2022) · Patterns · doi:10.1016/j.patter.2021.100429 · Cited by 4

Beyond the waves: what does the EEG still hold one century after H. Berger?

Peter‐Derex, L. (2021) · SLEEP · doi:10.1093/sleep/zsab165 · Cited by 4

SleepEEGpy: a Python-based software “wrapper” package to organize preprocessing, analysis, and visualization of sleep EEG dataPreprint

Belonosov, G., Falach, R., Schmidig, F. J., Aderka, M., Zhelezniakov, V., Shani‐Hershkovich, R., Bar, E., & Nir, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.17.572046 · Cited by 4

Optimizing automated sleep stage scoring of 5-s mini-epochs: a transfer learning study

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An ensemble method for improving robustness against the electrode contact problems in automated sleep stage scoring

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NCH Sleep DataBankPreprint

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An important step toward automation of polysomnography analyses

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Innovación en sueño

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Automated Scoring of Sleep and Associated Events

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