Automated sleep scoring: A review of the latest approaches

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000100 · Cited by 28,164 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,474 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

A manual for standardized terminology, techniques and scoring system for sleep stages in human subjects

Rechtschaffen, A. (1968) · Medical Entomology and Zoology · Cited by 11,772 · see citing works

Large-Scale Video Classification with Convolutional Neural Networks

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354 · see citing works

A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects

Hobson, J. A. (1969) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(69)90021-2 · Cited by 5,215 · see citing works

DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG

Supratak, A., Dong, H., Wu, C., & Guo, Y. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2721116 · Cited by 1,376 · see citing works

The Sleep Heart Health Study: Design, Rationale, and Methods

(1997) · Sleep · doi:10.1093/sleep/20.12.1077 · Cited by 1,331 · see citing works

Regional Slow Waves and Spindles in Human Sleep

Nir, Y., Staba, R. J., Andrillon, T., Vyazovskiy, V. V., Cirelli, C., Fried, I., & Tononi, G. (2011) · Neuron · doi:10.1016/j.neuron.2011.02.043 · Cited by 924 · see citing works

37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society—EMBC 2015

(2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2417514 · Cited by 706 · see citing works

2014 International Joint Conference on Neural Networks (IJCNN)

(2014) · doi:10.1109/ijcnn32435.2014 · Cited by 699 · see citing works

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., & Gramfort, A. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2813138 · Cited by 632 · see citing works

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Phan, H., Andreotti, F., Cooray, N., Chén, O. Y., & Vos, M. D. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2896659 · Cited by 631 · see citing works

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

(2018) · doi:10.1109/embc38410.2018 · Cited by 498 · see citing works

The American Academy of Sleep Medicine Inter-scorer Reliability Program: Sleep Stage Scoring

Rosenberg, R. S., & Van Hout, S. (2013) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.2350 · Cited by 456 · see citing works

Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography - a systematic review

van de Water, A. T. M., HOLMES, A., & Hurley, D. A. (2010) · Journal of Sleep Research · doi:10.1111/j.1365-2869.2009.00814.x · Cited by 454 · see citing works

Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier

Fraiwan, L., Lweesy, K. Y., Khasawneh, N., Wenz, H., & Dickhaus, H. (2011) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2011.11.005 · Cited by 433 · see citing works

A convolutional neural network for sleep stage scoring from raw single-channel EEG

Sors, A., Bonnet, S., Mirek, S., Vercueil, L., & Payen, J. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.12.001 · Cited by 425 · see citing works

Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard

DANKER‐HOPFE, H., ANDERER, P., ZEITLHOFER, J., BOECK, M., DORN, H., GRUBER, G., HELLER, E., LORETZ, E., MOSER, D., PARAPATICS, S., et al. (2009) · Journal of Sleep Research · doi:10.1111/j.1365-2869.2008.00700.x · Cited by 417 · see citing works

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

Stephansen, J. B., Olesen, A. N., Olsen, M., Ambati, A., Leary, E. B., Moore, H. E., Carrillo, O., Lin, L., Han, F., Yan, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07229-3 · Cited by 414 · see citing works

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon, K. A. I., Faezipour, M., Almuhammadi, W. S., & Moslehpour, S. (2016) · Entropy · doi:10.3390/e18090272 · Cited by 387 · see citing works

Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal

Zhu, G., Li, Y., & Wen, P. P. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2303991 · Cited by 381 · see citing works

Editorial Overview —The Role of Information Systems in Healthcare: Current Research and Future Trends

(2011) · Information Systems Research · doi:10.1287/isre.1110.0382 · Cited by 365 · see citing works

Montreal Archive of Sleep Studies: an open‐access resource for instrument benchmarking and exploratory research

O’Reilly, C., Gosselin, N., Carrier, J., & Nielsen, T. (2014) · Journal of Sleep Research · doi:10.1111/jsr.12169 · Cited by 358 · see citing works

Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders

Tsinalis, O., Matthews, P. M., & Guo, Y. (2015) · Annals of Biomedical Engineering · doi:10.1007/s10439-015-1444-y · Cited by 322 · see citing works

A comparative review on sleep stage classification methods in patients and healthy individuals

Boostani, R., Karimzadeh, F., & Nami, M. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.12.004 · Cited by 319 · see citing works

Epidemiological Overview of sleep Disorders in the General Population

Ohayon, M. M. (2011) · Sleep Medicine Research · doi:10.17241/smr.2011.2.1.1 · Cited by 311 · see citing works

Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models

Liang, S., Kuo, C., Hu, Y., Pan, Y.-H., & Wang, Y.-H. (2012) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2187242 · Cited by 304 · see citing works

Automatic sleep stage recurrent neural classifier using energy features of EEG signals

Hsu, Y., Yang, Y., Wang, J.-S., & Hsu, C. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.11.003 · Cited by 298 · see citing works

Mixed Neural Network Approach for Temporal Sleep Stage Classification

Dong, H., Supratak, A., Pan, W., Wu, C., Matthews, P. M., & Guo, Y. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2733220 · Cited by 294 · see citing works

Expert-level sleep scoring with deep neural networks

Biswal, S., Sun, H., Goparaju, B., Westover, M. B., Sun, J., & Bianchi, M. T. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy131 · Cited by 290 · see citing works

An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database

Anderer, P., Gruber, G., Parapatics, S., Woertz, M., Miazhynskaia, T., Klösch, G., Saletu, B., Zeitlhofer, J., Barbanoj, M. J., Danker‐Hopfe, H., et al. (2005) · Neuropsychobiology · doi:10.1159/000085205 · Cited by 285 · see citing works

A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms

Şen, B., Peker, M., Çavuşoğlu, A., & Çelebi, F. V. (2014) · Journal of Medical Systems · doi:10.1007/s10916-014-0018-0 · Cited by 282 · see citing works

A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Yıldırım, Ö., Baloğlu, U. B., & Acharya, U. R. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16040599 · Cited by 265 · see citing works

Signal processing techniques applied to human sleep EEG signals—A review

Motamedi‐Fakhr, S., Moshrefi‐Torbati, M., Hill, M., Hill, C. M., & White, P. R. (2014) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2013.12.003 · Cited by 264 · see citing works

Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural NetworksPreprint

Tsinalis, O., Matthews, P. M., Guo, Y., & Zafeiriou, S. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.01683 · Cited by 263 · see citing works

Computer based sleep recording and analysis

Penzel, T., & Conradt, R. (2000) · Sleep Medicine Reviews · doi:10.1053/smrv.1999.0087 · Cited by 235 · see citing works

Cloud computing-enabled healthcare opportunities, issues, and applications: A systematic review

Ali, O., Shrestha, A., Soar, J., & Wamba, S. F. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.07.009 · Cited by 233 · see citing works

Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting

Güneş, S., Polat, K., & Yosunkaya, Ş. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.04.043 · Cited by 229 · see citing works

Sleep scoring using artificial neural networks

Ronzhina, M., Janoušek, O., Kolářová, J., Novàkovâ, M., Honzík, P., & Provazník, I. (2011) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2011.06.003 · Cited by 229 · see citing works

Performance of an Automated Polysomnography Scoring System Versus Computer-Assisted Manual Scoring

Malhotra, A., Younes, M. K., Kuna, S. T., Benca, R. M., Kushida, C. A., Walsh, J. A., Hanlon, A. L., Staley, B. A., Pack, A. I., & Pien, G. W. (2013) · SLEEP · doi:10.5665/sleep.2548 · Cited by 224 · see citing works

Computer-assisted sleep staging

Agarwal, R., & Gotman, J. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.966600 · Cited by 219 · see citing works

Odds Ratio Product of Sleep EEG as a Continuous Measure of Sleep State

Younes, M. K., Ostrowski, M., Soiferman, M., Younes, H., Younes, M., Raneri, J. K., & Hanly, P. J. (2015) · SLEEP · doi:10.5665/sleep.4588 · Cited by 203 · see citing works

Sleep stage classification using single-channel EOG

Rahman, M. M., Bhuiyan, M. I. H., & Hassan, A. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.08.022 · Cited by 178 · see citing works

Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring

Vilamala, A., Madsen, K. H., & Hansen, L. K. (2017) · Machine learning for signal processing ... · doi:10.1109/mlsp.2017.8168133 · Cited by 171 · see citing works

Participatory Design, User Involvement and Health IT Evaluation

Andre, K., & Christian, N. (2016) · Studies in Health Technology and Informatics · doi:10.3233/978-1-61499-635-4-139 · Cited by 166 · see citing works

An end-to-end framework for real-time automatic sleep stage classification

Patanaik, A., Ong, J. L., Gooley, J. J., Ancoli‐Israel, S., & Chee, M. W. L. (2018) · SLEEP · doi:10.1093/sleep/zsy041 · Cited by 162 · see citing works

38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

(2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2462411 · Cited by 160 · see citing works

ANALYSIS AND AUTOMATIC IDENTIFICATION OF SLEEP STAGES USING HIGHER ORDER SPECTRA

Acharya, U. R., Chua, E. C.-P., Chua, K. C., Min, L. C., & TAMURA, T. (2010) · International Journal of Neural Systems · doi:10.1142/s0129065710002589 · Cited by 153 · see citing works

Automatic Human Sleep Stage Scoring Using Deep Neural Networks

Malafeev, A., Laptev, D., Bauer, S., Omlin, X., Wierzbicka, A., Wichniak, A., Jernajczyk, W., Riener, R., Buhmann, J., & Achermann, P. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00781 · Cited by 151 · see citing works

Staging Sleep in Polysomnograms: Analysis of Inter-Scorer Variability

Younes, M. K., Raneri, J. K., & Hanly, P. J. (2016) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.5894 · Cited by 134 · see citing works

SLEEPNET: Automated Sleep Staging System via Deep LearningPreprint

Biswal, S., Kulas, J. A., Sun, H., Goparaju, B., Westover, M. B., Bianchi, M. T., & Sun, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.08262 · Cited by 134 · see citing works

Automatic sleep stage classification using two-channel electro-oculography

Virkkala, J., Hasan, J., Värri, A. O., Himanen, S., & Müller, K. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.06.016 · Cited by 130 · see citing works

Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters

Stepnowsky, C. J., Levendowski, D. J., Popovic, D. P., Ayappa, I. A., & Rapoport, D. M. (2013) · Sleep Medicine · doi:10.1016/j.sleep.2013.04.022 · Cited by 115 · see citing works

Computer-Assisted Sleep Classification according to the Standard of the American Academy of Sleep Medicine: Validation Study of the AASM Version of the Somnolyzer 24 × 7

Anderer, P., Moreau, A., Woertz, M., Ross, M., Gruber, G., Parapatics, S., Loretz, E., Heller, E., Schmidt, A., Boeck, M., et al. (2010) · Neuropsychobiology · doi:10.1159/000320864 · Cited by 115 · see citing works

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

Huang, C.-S., Lin, C.-L., Liwei, K., Liu, S.-Y., Su, T.-P., & Lin, C.-T. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00263 · Cited by 93 · see citing works

Assessment of Automated Scoring of Polysomnographic Recordings in a Population with Suspected Sleep-disordered Breathing

Pittman, S. D., MacDonald, M., Fogel, R., Malhotra, A., Todros, K., Levy, B., Geva, A. B., & White, D. P. (2004) · SLEEP · doi:10.1093/sleep/27.7.1394 · Cited by 89 · see citing works

Development of an EOG-Based Automatic Sleep-Monitoring Eye Mask

Liang, S., Kuo, C., Lee, Y., Lin, W., Liu, Y., Chen, P., Cherng, F., & Shaw, F. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2015.2433652 · Cited by 89 · see citing works

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

Lweesy, K. Y., Khasawneh, N., Fraiwan, M., Wenz, H., Dickhaus, H., & Fraiwan, L. (2010) · Methods of Information in Medicine · doi:10.3414/me09-01-0054 · Cited by 85 · see citing works

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

(2016) · doi:10.1109/mlsp38331.2016 · Cited by 79 · see citing works

Automatic Sleep Stage Classification Based on Convolutional Neural Network and Fine‐Grained Segments

Cui, Z., Zheng, X., Shao, X., & Cui, L. (2018) · Complexity · doi:10.1155/2018/9248410 · Cited by 75 · see citing works

Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal

Radha, M., Garcia‐Molina, G., Poel, M., & Tononi, G. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943976 · Cited by 68 · see citing works

Computer-Assisted Automated Scoring of Polysomnograms Using the Somnolyzer System

Punjabi, N. M., Shifa, N., Dorffner, G., Patil, S. P., Pien, G. W., & Aurora, R. N. (2015) · SLEEP · doi:10.5665/sleep.5046 · Cited by 67 · see citing works

Mutual information measures applied to EEG signals for sleepiness characterization

Melia, U. S. P., Guaita, M., Vallverdú, M., Embid, C., Vilaseca, I., Salamero, M., & Santamaría, J. (2015) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2015.01.002 · Cited by 64 · see citing works

Reliability of the American Academy of Sleep Medicine Rules for Assessing Sleep Depth in Clinical Practice

Younes, M. K., Kuna, S. T., Pack, A. I., Walsh, J. K., Kushida, C. A., Staley, B. A., & Pien, G. W. (2018) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.6934 · Cited by 64 · see citing works

Complex-valued unsupervised convolutional neural networks for sleep stage classification

Zhang, J., & Wu, Y. X. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.07.015 · Cited by 61 · see citing works

Evaluation of Automated and Semi-Automated Scoring of Polysomnographic Recordings from a Clinical Trial Using Zolpidem in the Treatment of Insomnia

Svetnik, V., Ma, J., Soper, K. A., Doran, S. M., Renger, J. J., Deacon, S., & Koblan, K. S. (2007) · SLEEP · doi:10.1093/sleep/30.11.1562 · Cited by 59 · see citing works

The case for using digital EEG analysis in clinical sleep medicine

Younes, M. K. (2017) · Sleep Science and Practice · doi:10.1186/s41606-016-0005-0 · Cited by 58 · see citing works

Automatic scoring of sleep stages and cortical arousals using two electrodes on the forehead: validation in healthy adults

Popovic, D. P., Khoo, M. C. K., & Westbrook, P. R. (2013) · Journal of Sleep Research · doi:10.1111/jsr.12105 · Cited by 53 · see citing works

Utility of Technologist Editing of Polysomnography Scoring Performed by a Validated Automatic System

Younes, M. K., Thompson, W., Leslie, C., Egan, T., & Giannouli, E. (2015) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201411-512oc · Cited by 34 · see citing works

Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

Olesen, A. N., Jennum, P., Peppard, P., Mignot, E. J.-M., & Sorensen, H. B. D. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513080 · Cited by 33 · see citing works

Electrooculogram based sleep stage classification using deep belief network

Xia, B., Li, Q., Jia, J., Wang, J., Chaudhary, U., Ramos‐Murguialday, A., & Birbaumer, N. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280775 · Cited by 28 · see citing works

Automatic Sleep Scoring in Normals and in Individuals With Neurodegenerative Disorders According to New International Sleep Scoring Criteria

Jensen, P. S., Sørensen, H. B. D., Leonthin, H. L., & Jennum, P. (2010) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3181eaad4b · Cited by 28 · see citing works

From Usability Engineering to Evidence-based Usability in Health IT

Romaric, M., Peute, L., & Beuscart-Zéphir, M.-C. (2016) · Studies in health technology and informatics · doi:10.3233/978-1-61499-635-4-126 · Cited by 24 · see citing works

Semi-automatic sleep EEG scoring based on the hypnospectrogram

Koupparis, A. M., Kokkinos, V., & Kostopoulos, G. (2013) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.10.010 · Cited by 21 · see citing works

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning

Olesen, A. N., Christensen, J. A. E., Sørensen, H. B. D., & Jennum, P. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591548 · Cited by 19 · see citing works

Conscious experience in sleep and wakefulness

Siclari, F., Bassetti, C. L., & Tononi, G. (2013) · Schweizer Archiv für Neurologie und Psychiatrie · doi:10.4414/sanp.2012.00137 · Cited by 10 · see citing works

0315 Inter- And Intra-expert Variability In Sleep Scoring: Comparison Between Visual And Automatic Analysis

Muto, V., Berthomier, C., Schmidt, C., Vandewalle, G., Jaspar, M., Devillers, J., Chellappa, S. L., Meyer, C., Phillips, C. L. M., Berthomier, P., et al. (2018) · SLEEP · doi:10.1093/sleep/zsy061.314 · Cited by 7 · see citing works

Feature extraction feature selection and dimensionality reduction techniques for brain computer interface

Lan, T. (2011) · doi:10.6083/m4rx9920 · Cited by 5 · see citing works

Untitled work

(2014) · doi:10.4230/oasics.slate.2021.16 · Cited by 1 · see citing works

Assessing competency of Z3Score automated sleep stage scoring system with manual sleep stage scoring by multiple scorers

Tay, J. Y., Toh, S., Leow, L. C., & Senin, S. R. (2017) · Sleep Medicine · doi:10.1016/j.sleep.2017.11.957 · Cited by 1 · see citing works

Addressing bias in big data and AI for health care: A call for open science

Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100347 · Cited by 903 · see citing works

An open-source, high-performance tool for automated sleep staging

Vallat, R., & Walker, M. P. (2021) · eLife · doi:10.7554/elife.70092 · Cited by 366 · see citing works

Interrater reliability of sleep stage scoring: a meta-analysis

Lee, Y. J., Lee, J. Y., Cho, J. H., & Choi, J. H. (2021) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.9538 · Cited by 178 · see citing works

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

Phan, H., Chén, O. Y., Koch, P., Lu, Z., McLoughlin, I. V., Mertins, A., & Vos, M. D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3020381 · Cited by 153 · see citing works

Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond

Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152 · see citing works

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

Phan, H., & Mikkelsen, K. B. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac6049 · Cited by 145 · see citing works

Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification

Zhu, T., Luo, W., & Yu, F. N. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17114152 · Cited by 138 · see citing works

DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model With Uncertainty Estimates

Fiorillo, L., Favaro, P., & Faraci, F. D. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3117970 · Cited by 126 · see citing works

Multi-Night Validation of a Sleep Tracking Ring in Adolescents Compared with a Research Actigraph and Polysomnography

Chee, N. I. Y. N., Ghorbani, S., Golkashani, H. A., Leong, R. L. F., Ong, J. L., & Chee, M. W. L. (2021) · Nature and Science of Sleep · doi:10.2147/nss.s286070 · Cited by 108 · see citing works

Clinical applications of artificial intelligence in sleep medicine: A sleep clinician’s perspective

Bandyopadhyay, A., & Goldstein, C. (2023) · Sleep And Breathing · doi:10.1007/s11325-022-02592-4 · Cited by 108 · see citing works

Shaping the slow waves of sleep: A systematic and integrative review of sleep slow wave modulation in humans using non-invasive brain stimulation

Fehér, K. D., Wunderlin, M., Maier, J. G., Hertenstein, E., Schneider, C. L., Mikutta, C. A., Züst, M. A., Klöppel, S., & Nissen, C. (2021) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2021.101438 · Cited by 102 · see citing works

Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

Olesen, A. N., Jennum, P., Mignot, E. J.-M., & Sørensen, H. B. D. (2020) · SLEEP · doi:10.1093/sleep/zsaa161 · Cited by 94 · see citing works

Artificial intelligence and sleep: Advancing sleep medicine

Watson, N. F., & Fernandez, C. (2021) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2021.101512 · Cited by 78 · see citing works

Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography

Fu, M., Wang, Y., Chen, Z., Li, J., Xu, F., Liu, X., & Hou, F. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.628502 · Cited by 71 · see citing works

Sleep timing and the circadian clock in mammals: Past, present and the road ahead

Sanchez, R. E. A., Kalume, F. K., & de la Iglesia, H. O. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.05.034 · Cited by 71 · see citing works

Sleep scoring in rodents: Criteria, automatic approaches and outstanding issues

Rayan, A., Agarwal, A., Samanta, A., Severijnen, E., van der Meij, J., & Genzel, L. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15884 · Cited by 62 · see citing works

An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea

Vaquerizo-Villar, F., Gutiérrez‐Tobal, G. C., Merino, E. C., Álvarez, D., Gozal, D., del Campo, F., Gozal, D., & Hornero, R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107419 · Cited by 63 · see citing works

A large collection of real-world pediatric sleep studies

Lee, H., Li, B., DeForte, S., Splaingard, M., Huang, Y., Chi, Y., & Linwood, S. L. (2022) · Scientific Data · doi:10.1038/s41597-022-01545-6 · Cited by 63 · see citing works

Adaptive Single-Channel EEG Artifact Removal With Applications to Clinical Monitoring

Dora, M., & Holcman, D. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3147072 · Cited by 62 · see citing works

Do not sleep on traditional machine learning

Donckt, J. V. D., Donckt, J. V. D., Deprost, E., Vandenbussche, N., Rademaker, M., Vandewiele, G., & Hoecke, S. V. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104429 · Cited by 61 · see citing works

SHNN: A single-channel EEG sleep staging model based on semi-supervised learning

Zhang, Y., Cao, W., Feng, L.-X., Wang, M., Geng, T., Zhou, J., & Gao, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119288 · Cited by 59 · see citing works

Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series

Decat, N., Walter, J., Koh, Z. H., Sribanditmongkol, P., Fulcher, B., Windt, J., Andrillon, T., & Tsuchiya, N. (2022) · Sleep Medicine · doi:10.1016/j.sleep.2022.06.013 · Cited by 54 · see citing works

Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points

Xu, Z., Yang, X., Sun, J., Liu, P., & Qin, W. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00014 · Cited by 56 · see citing works

MaskSleepNet: A Cross-Modality Adaptation Neural Network for Heterogeneous Signals Processing in Sleep Staging

Zhu, H., Zhou, W., Fu, C., Wu, Y., Shen, N., Shu, F., Yu, H., Chen, W., & Chen, C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3253728 · Cited by 53 · see citing works

Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence–based Stanford-STAGES algorithm

Cesari, M., Stefani, A., Penzel, T., Ibrahim, A., Hackner, H., Heidbreder, A., Szentkirályi, A., Stubbe, B., Völzke, H., Berger, K., et al. (2021) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.9174 · Cited by 52 · see citing works

Wake slow waves in focal human epilepsy impact network activity and cognition

Sheybani, L., Vivekananda, U., Rodionov, R., Diehl, B., Chowdhury, F. A., McEvoy, A. W., Miserocchi, A., Bisby, J. A., Bush, D., Burgess, N., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42971-3 · Cited by 51 · see citing works

U-Sleep’s resilience to AASM guidelines

Fiorillo, L., Monachino, G., van der Meer, J., Pesce, M., Warncke, J. D., Schmidt, M. H., Bassetti, C. L., Tzovara, A., Favaro, P., & Faraci, F. D. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00784-0 · Cited by 50 · see citing works

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

Sharma, M. D., Patel, V., Tiwari, J., & Acharya, U. R. (2021) · Diagnostics · doi:10.3390/diagnostics11081380 · Cited by 48 · see citing works

Spotlight on Sleep Stage Classification Based on EEG

Lambert, I., & Peter‐Derex, L. (2023) · Nature and Science of Sleep · doi:10.2147/nss.s401270 · Cited by 49 · see citing works

Current status and prospects of automatic sleep stages scoring: Review

Gaiduk, M., Alarcón, Á. S., Seepold, R., & Madrid, N. M. (2023) · Biomedical Engineering Letters · doi:10.1007/s13534-023-00299-3 · Cited by 46 · see citing works

The Future of Sleep Measurements

Arnardóttir, E. S., Íslind, A. S., & Óskarsdóttir, M. (2021) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2021.05.004 · Cited by 45 · see citing works

Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep

Rentz, L. E., Ulman, H. K., & Galster, S. M. (2021) · Sensors · doi:10.3390/s21155071 · Cited by 46 · see citing works

Diagnosis of Sleep Apnoea Using a Mandibular Monitor and Machine Learning Analysis: One-Night Agreement Compared to in-Home Polysomnography

Kelly, J. L., Messaoud, R. B., Joyeux‐Faure, M., Terrail, R., Tamisier, R., Martinot, J., Lê-Dông, N.-N., Morrell, M. J., & Pépin, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.726880 · Cited by 45 · see citing works

Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification

Kim, D., Lee, J., Woo, Y., Jeong, J., Kim, C., & Kim, C. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020136 · Cited by 46 · see citing works

Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape

Djanian, S., Bruun, A., & Nielsen, T. D. (2022) · Sleep Medicine · doi:10.1016/j.sleep.2022.09.004 · Cited by 45 · see citing works

Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders

Peter‐Derex, L., Berthomier, C., Taillard, J., Berthomier, P., Bouet, R., Mattout, J., Brandewinder, M., & Bastuji, H. (2020) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.8864 · Cited by 44 · see citing works

SleepSEEG: automatic sleep scoring using intracranial EEG recordings only

von Ellenrieder, N., Peter‐Derex, L., Gotman, J., & Frauscher, B. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac6829 · Cited by 43 · see citing works

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data

Casciola, A. A., Carlucci, S. K., Kent, B. A., Punch, A. M., Muszynski, M. A., Zhou, D. J., Kazemi, A., Mirian, M. S., Valerio, J., McKeown, M. J., et al. (2021) · Sensors · doi:10.3390/s21103316 · Cited by 39 · see citing works

An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects

Sharma, M. D., Yadav, A., Tiwari, J., Karabatak, M., Yıldırım, Ö., & Acharya, U. R. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19127176 · Cited by 38 · see citing works

Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning

Cheng, Y.-H., Lech, M., & Wilkinson, R. H. (2023) · Sensors · doi:10.3390/s23073468 · Cited by 37 · see citing works

Exploring scoring methods for research studies: Accuracy and variability of visual and automated sleep scoring

Berthomier, C., Muto, V., Schmidt, C., Vandewalle, G., Jaspar, M., Devillers, J., Gaggioni, G., Chellappa, S. L., Meyer, C., Phillips, C. L. M., et al. (2020) · Journal of Sleep Research · doi:10.1111/jsr.12994 · Cited by 37 · see citing works

A CNN identified by reinforcement learning-based optimization framework for EEG-based state evaluation

Yang, Y., Gao, Z., Li, Y., & Wang, H. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abfa71 · Cited by 37 · see citing works

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Lechat, B., Scott, H., Naik, G. R., Hansen, K. L., Nguyen, D. P., Vakulin, A., CATCHESIDE, P. G., & Eckert, D. J. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.751730 · Cited by 38 · see citing works

Representations of temporal sleep dynamics: Review and synthesis of the literature

Hermans, L. W., Huijben, I. A. M., van Gorp, H., Leufkens, T., Fonseca, P. M., Overeem, S., & van Gilst, M. M. (2022) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2022.101611 · Cited by 37 · see citing works

Automatic sleep stage classification using deep learning: signals, data representation, and neural networks

Liu, P., Qian, W., Zhang, H., Zhu, Y., Hong, Q., Li, Q., & Yao, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10926-9 · Cited by 39 · see citing works

MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG

Yu, R., Zhou, Z., Wu, S., Gao, X., & Bin, G. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/aca2de · Cited by 35 · see citing works

A Systematic Review of Literature on Automated Sleep Scoring

Alsolai, H., Qureshi, S., Iqbal, S. M. Z., Vanichayobon, S., Henesey, L. E., Lindley, C. A., & Karrila, S. (2022) · IEEE Access · doi:10.1109/access.2022.3194145 · Cited by 34 · see citing works

Sleep apnea and respiratory anomaly detection from a wearable band and oxygen saturation

Ganglberger, W., Bucklin, A. A., Tesh, R. A., Cardoso, M. D. S., Sun, H., Leone, M. J., Paixão, L., Panneerselvam, E., Ye, E. M., Thompson, B., et al. (2021) · Sleep And Breathing · doi:10.1007/s11325-021-02465-2 · Cited by 33 · see citing works

Differentiation of central disorders of hypersomnolence with manual and artificial-intelligence-derived polysomnographic measures

Cesari, M., Egger, K., Stefani, A., Bergmann, M., Ibrahim, A., Brandauer, E., Högl, B., & Heidbreder, A. (2022) · SLEEP · doi:10.1093/sleep/zsac288 · Cited by 33 · see citing works

A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep

Leach, S., Chung, K.-Y., Tüshaus, L., Huber, R., & Karlen, W. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00586 · Cited by 32 · see citing works

Certainty about uncertainty in sleep staging: a theoretical framework

van Gorp, H., Huijben, I. A. M., Fonseca, P. M., van Sloun, R. J. G., Overeem, S., & van Gilst, M. M. (2022) · SLEEP · doi:10.1093/sleep/zsac134 · Cited by 31 · see citing works

Automatic Sleep Staging Based on Contextual Scalograms and Attention Convolution Neural Network Using Single-Channel EEG

Wei, Y., Zhu, Y., Zhou, Y., Yu, X., & Luo, Y. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3332503 · Cited by 31 · see citing works

A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis

Pan, J., Wu, J., Liu, J., Wu, J., & Wang, F. (2021) · Brain Sciences · doi:10.3390/brainsci11081072 · Cited by 30 · see citing works

Diagnostic accuracy of level IV portable sleep monitors versus polysomnography for pediatric obstructive sleep apnea: a systematic review and meta-analysis

Gao, X., Li, Y., Xu, W., & Han, D.-M. (2021) · Sleep Medicine · doi:10.1016/j.sleep.2021.08.029 · Cited by 30 · see citing works

Auto-annotating sleep stages based on polysomnographic data

Zhang, H., Wang, X., Li, H., Mehendale, S., & Guan, Y. (2021) · Patterns · doi:10.1016/j.patter.2021.100371 · Cited by 30 · see citing works

AI-Driven sleep staging from actigraphy and heart rate

Song, T.-A., Chowdhury, S. R., Malekzadeh, M., Harrison, S. L., Blackwell-Hoge, T., Redline, S. S., Stone, K. L., Saxena, R., Purcell, S., & Dutta, J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285703 · Cited by 29 · see citing works

An intelligent model involving multi-channels spectrum patterns based features for automatic sleep stage classification

Abdulla, S., Diykh, M., Siuly, S., & Ali, M. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105001 · Cited by 28 · see citing works

A novel deep learning model for obstructive sleep apnea diagnosis: hybrid CNN-Transformer approach for radar-based detection of apnea-hypopnea events

Choi, J. W., Koo, D. L., Kim, D. H., Nam, H., Lee, J. H., Hong, S., & Kim, B. (2024) · SLEEP · doi:10.1093/sleep/zsae184 · Cited by 27 · see citing works

Benchmarking performance of an automatic polysomnography scoring system in a population with suspected sleep disorders

Choo, B. P., Mok, Y., Oh, H. C., Patanaik, A., Kishan, K., Awasthi, A., Biju, S., Bhattacharjee, S., Poh, Y., & Wong, H. S. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1123935 · Cited by 27 · see citing works

Automated scoring of pre-REM sleep in mice with deep learning

Grieger, N., Schwabedal, J. T. C., Wendel, S., Ritze, Y., & Bialonski, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-91286-0 · Cited by 26 · see citing works

Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures

Hunter, L. B., Baten, A., Haskell, M. J., Langford, F. M., O’Connor, C., Webster, J. R., & Stafford, K. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-90416-y · Cited by 26 · see citing works

Sleep insufficiency, circadian rhythms, and metabolomics: the connection between metabolic and sleep disorders

Russell, K. L., Rodman, H. R., & Pak, V. M. (2023) · Sleep And Breathing · doi:10.1007/s11325-023-02828-x · Cited by 27 · see citing works

Classification at the accuracy limit: facing the problem of data ambiguity

Metzner, C., Schilling, A., Traxdorf, M., Tziridis, K., Maier, A., Schulze, H., & Krauß, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-26498-z · Cited by 25 · see citing works

Multi-scored sleep databases: how to exploit the multiple-labels in automated sleep scoring

Fiorillo, L., Pedroncelli, D., Agostini, V., Favaro, P., & Faraci, F. D. (2023) · SLEEP · doi:10.1093/sleep/zsad028 · Cited by 25 · see citing works

Deep transfer learning for automated single-lead EEG sleep staging with channel and population mismatches

van der Aar, J. F., van den Ende, D., Fonseca, P. M., van Meulen, F., Overeem, S., van Gilst, M. M., & Peri, E. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2023.1287342 · Cited by 23 · see citing works

A continuous approach to explain insomnia and subjective-objective sleep discrepancy

Herzog, R. A., Crosbie, F., Aloulou, A., Hanif, U., Chennaoui, M., Léger, D., & Andrillon, T. (2025) · Communications Biology · doi:10.1038/s42003-025-07794-6 · Cited by 22 · see citing works

An Attention-Guided Spatiotemporal Graph Convolutional Network for Sleep Stage Classification

Li, M., Chen, H., & Cheng, Z. (2022) · Life · doi:10.3390/life12050622 · Cited by 22 · see citing works

Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review

Almutairi, H., Hassan, G. M., & Datta, A. (2023) · Applied Sciences · doi:10.3390/app132413280 · Cited by 22 · see citing works

Reliable automatic sleep stage classification based on hybrid intelligence

Shao, Y., Huang, B., Du, L., Wang, P., Li, Z., Liu, Z., Zhou, L., Song, Y., Chen, X., & Fang, Z. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108314 · Cited by 22 · see citing works

Automatic Detection of Microsleep Episodes With Deep Learning

Malafeev, A., Hertig-Godeschalk, A., Schreier, D. R., Skoručak, J., Mathis, J., & Achermann, P. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.564098 · Cited by 24 · see citing works

Automated sleep scoring system using multi-channel data and machine learning

Arslan, R. S., Ulutaş, H., Köksal, A. S., Bakır, M., & Çiftçi, B. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105653 · Cited by 21 · see citing works

Sleep in Disorders of Consciousness: A Brief Overview on a Still under Investigated Issue

Raciti, L., Raciti, G., Militi, D., Tonin, P., Quartarone, A., & Calabrò, R. S. (2023) · Brain Sciences · doi:10.3390/brainsci13020275 · Cited by 21 · see citing works

Asymmetry of sleep electrophysiological markers in patients with focal epilepsy

Sheybani, L., Mégevand, P., Roehri, N., Spinelli, L., Kleinschmidt, A. K., van Mierlo, P., Seeck, M., & Vulliémoz, S. (2023) · Brain Communications · doi:10.1093/braincomms/fcad161 · Cited by 21 · see citing works

SAGSleepNet: A deep learning model for sleep staging based on self-attention graph of polysomnography

Jin, Z., & Jia, K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105062 · Cited by 21 · see citing works

The Masking Impact of Intra-Artifacts in EEG on Deep Learning-Based Sleep Staging Systems: A Comparative Study

Zhu, H., Wu, Y., Shen, N., Fan, J., Tao, L., Fu, C., Yu, H., Wan, F., Pun, S. H., Chen, C., et al. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3173994 · Cited by 20 · see citing works

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis

Tăuţan, A.-M., Rossi, A. C., de Francisco, R., & Ionescu, B. E. (2020) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2020-0139 · Cited by 20 · see citing works

Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features

Aellen, F. M., Göktepe-Kavis, P., Apostolopoulos, S., & Tzovara, A. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109367 · Cited by 18 · see citing works

Behavioral and physiological monitoring for awake neurovascular coupling experiments: a how-to guide

Zhang, Q., Turner, K. L., Gheres, K. W., Hossain, M. S., & Drew, P. J. (2022) · Neurophotonics · doi:10.1117/1.nph.9.2.021905 · Cited by 18 · see citing works

ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training

Jirakittayakorn, N., Wongsawat, Y., & Mitrirattanakul, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-60796-y · Cited by 18 · see citing works

Automatic Sleep Stage Detection: A Study on the Influence of Various PSG Input Signals

Tăuţan, A.-M., Rossi, A. C., de Francisco, R., & Ionescu, B. E. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175628 · Cited by 18 · see citing works

A deep learning approach to detect sleep stages

Stuburić, K., Gaiduk, M., & Seepold, R. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.09.280 · Cited by 18 · see citing works

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls

Somaskandhan, P., Leppänen, T., Terrill, P. I., Sigurdardottir, S. E., Arnardóttir, E. S., Olafsdottir, K. A., Serwatko, M., SIGURDARDOTTIR, S. T., Clausen, M. V., Töyräs, J., et al. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1162998 · Cited by 17 · see citing works

The pros and cons of using automated sleep scoring in sleep research

Rayan, A., Szabo, A. B., & Genzel, L. (2023) · SLEEP · doi:10.1093/sleep/zsad275 · Cited by 17 · see citing works

Computer-assisted analysis of polysomnographic recordings improves inter-scorer associated agreement and scoring times

Álvarez-Estévez, D., & Rijsman, R. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0275530 · Cited by 17 · see citing works

Interpretation and further development of the hypnodensity representation of sleep structure

Huijben, I. A. M., Hermans, L. W., Rossi, A. C., Overeem, S., van Gilst, M. M., & van Sloun, R. J. G. (2022) · Physiological Measurement · doi:10.1088/1361-6579/aca641 · Cited by 17 · see citing works

Artificial intelligence and sleep medicine II: A scoping review of applications, advancements, and future directions

Jahrami, H., Husain, W., Trabelsi, K., Penzel, T., Hirshkowitz, M., Razjouyan, J., BaHammam, A. S., & Sharafkhaneh, A. (2025) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2025.102212 · Cited by 16 · see citing works

Extracting continuous sleep depth from EEG data without machine learning

Metzner, C., Schilling, A., Traxdorf, M., Schulze, H., Tziridis, K., & Krauß, P. (2023) · Neurobiology of Sleep and Circadian Rhythms · doi:10.1016/j.nbscr.2023.100097 · Cited by 16 · see citing works

Multi-task learning for arousal and sleep stage detection using fully convolutional networks

Zan, H., & Yildiz, A. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acfe3a · Cited by 17 · see citing works

Automated Scoring of Sleep and Associated Events

Anderer, P., Ross, M., Cerny, A., & Shaw, E. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-06413-5_7 · Cited by 16 · see citing works

Can portable sleep monitors replace polysomnography for diagnosis of pediatric OSA: a systematic review and meta-analysis

Tuohuti, A., Lin, Z., Cai, J., & Chen, X. (2023) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-023-08095-6 · Cited by 15 · see citing works

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

Brodersen, P. J. N., Alfonsa, H., Krone, L. B., Blanco‐Duque, C., Fisk, A. S., Flaherty, S. J., Guillaumin, M. C. C., Huang, Y., Kahn, M. C., McKillop, L. E., et al. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011793 · Cited by 16 · see citing works

Sleep modelled as a continuous and dynamic process predicts healthy ageing better than traditional sleep scoring

Cesari, M., Stefani, A., Mitterling, T., Frauscher, B., Schönwald, S. V., & Högl, B. (2020) · Sleep Medicine · doi:10.1016/j.sleep.2020.11.033 · Cited by 15 · see citing works

Towards precision sleep medicine: Self-attention GAN as an innovative data augmentation technique for developing personalized automatic sleep scoring classification

Kuo, C., Lu, T., Chen, G.-T., & Liao, P.-Y. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105828 · Cited by 15 · see citing works

The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods

Zhu, H., Fu, C., Shu, F., Yu, H., Chen, C., & Chen, W. (2023) · Bioengineering · doi:10.3390/bioengineering10050573 · Cited by 15 · see citing works

An accessible and versatile deep learning-based sleep stage classifier

Hanna, J., & Flöel, A. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.1086634 · Cited by 14 · see citing works

Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications

Zubler, F., & Tzovara, A. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1183810 · Cited by 14 · see citing works

InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography

Nam, B., Bark, B., Lee, J., & Kim, I. Y. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02437-y · Cited by 16 · see citing works

Temporal complexity of EEG encodes human alertness

Hadra, M., Omidvarnia, A., & Mesbah, M. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac8f80 · Cited by 14 · see citing works

Generalizable Deep Learning-Based Sleep Staging Approach for Ambulatory Textile Electrode Headband Recordings

Rusanen, M., Huttunen, R., Korkalainen, H., Myllymaa, S., Töyräs, J., Myllymaa, K., Sigurdardottir, S. E., Olafsdottir, K. A., Leppänen, T., Arnardóttir, E. S., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3240437 · Cited by 14 · see citing works

Creating an Optimal Approach for Diagnosing Sleep Apnea

Pépin, J., Tamisier, R., Baillieul, S., Messaoud, R. B., Foote, A., Bailly, S., & Martinot, J. (2023) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2023.05.004 · Cited by 13 · see citing works

Automated sleep scoring, deep learning and physician supervision

Fiorillo, L. (2022) · BORIS Theses (Bern Open Research Information System) (Bern University Library, Hochschulstrasse 6, 3012 Bern, Switzerland) · doi:10.48549/4218

Automated Scoring of Sleep and Associated Events

Anderer, P., Ross, M., Cerny, A., & Shaw, E. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-06413-5_7 · Cited by 16 · see citing works

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

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