Fetching the paper…
Reading the bibliography…
Scoring sleep stages in polysomnography recordings is a time-consuming task plagued by significant inter-rater variability.
J. A. Hobson, A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects, Electroencephalography and clinical neurophysiology 26 (6) (1969) 644
1969
Earlier work this paper cites.
J. Schmidhuber, Learning complex, extended sequences using the principle of history compression, Neural Computation 4 (2) (1992) 234–242
1992
Earlier work this paper cites.
S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation 9 (8) (1997) 1735–1780
1997
Earlier work this paper cites.
S. F. Quan, B. V. Howard, C. Iber, J. P. Kiley, F. J. Nieto, G. T. O’Connor, D. M. Rapoport, S. Redline, J. Robbins, J. M. Samet, et al., The sleep heart health study: design, rationale, and methods, Sleep 20 (12) (1997) 1077–1085
1997
Earlier work this paper cites.
B. Kemp, A. H. Zwinderman, B. Tuk, H. A. Kamphuisen, J. J. Oberye, Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the eeg, IEEE Transactions on Biomedical Engineering 47 (9) (2000) 1185–1194
2000
Earlier work this paper cites.
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, H. E. Stanley, Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals, circulation 101 (23) (2000) e215–e220
2000
Earlier work this paper cites.
T. D. Lagerlund, Manipulating the magic of digital eeg: montage reformatting and filtering, American journal of electroneurodiagnostic technology 40 (2) (2000) 121–136
2000
Earlier work this paper cites.
C. Iber, S. Ancoli-Israel, A. L. Chesson, S. F. Quan, et al., The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, Vol. 1, American academy of sleep medicine Westchester, IL, 2007
2007
Earlier work this paper cites.
doi:10.1111/j.1365-2869.2008.00700.x
H. Danker-Hopfe, P. Anderer, J. Zeitlhofer, M. Boeck, H. Dorn, G. Gruber, E. Heller, E. Loretz, D. Moser, S. Parapatics, B. Saletu, A. Schmidt, G. Dorffner, Interrater reliability for sleep scoring according to the rechtschaffen & kales and the new AASM standard, Journal of Sleep Research 18 (1) (2009) 74–84 · 2008
Earlier work this paper cites.
A. Supratak, H. Dong, C. Wu, Y. Guo, Deepsleepnet: A model for automatic sleep stage scoring based on raw single-channel eeg, IEEE Transactions on Neural Systems and Rehabilitation Engineering 25 (11) (2017) 1998–2008
2008
Earlier work this paper cites.
L. A. Panossian, A. Y. Avidan, Review of sleep disorders, Medical Clinics of North America 93 (2) (2009) 407–425
2009
Earlier work this paper cites.
doi:10.5664/jcsm.2350
R. S. Rosenberg, S. V. Hout, The american academy of sleep medicine inter-scorer reliability program: Sleep stage scoring, Journal of Clinical Sleep Medicine 09 (01) (2013) 81–87 · 2013
Earlier work this paper cites.
doi:10.1111/jsr.12169
C. O’Reilly, N. Gosselin, J. Carrier, T. Nielsen, Montreal archive of sleep studies: an open-access resource for instrument benchmarking and exploratory research, Journal of Sleep Research 23 (6) (2014) 628–635 · 2014
Earlier work this paper cites.
J. O. Smith, Digital audio resampling home page (02 2015). URL http://ccrma.stanford.edu/~jos/resample/
2015
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, Advances in neural information processing systems 30 (2017)
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollár, Focal loss for dense object detection, in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 2980–2988
2017
Earlier work this paper cites.
L. N. Smith, Cyclical learning rates for training neural networks, in: 2017 IEEE winter conference on applications of computer vision (WACV), IEEE, 2017, pp. 464–472
2017
Cited alongside, same era.
J. B. Stephansen, A. N. Olesen, M. Olsen, A. Ambati, E. B. Leary, H. E. Moore, O. Carrillo, L. Lin, F. Han, H. Yan, et al., Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy, Nature communications 9 (1) (2018) 5229
2018
Cited alongside, same era.
I. Loshchilov, F. Hutter, Decoupled weight decay regularization, in: International Conference on Learning Representations, 2018
2018
Cited alongside, same era.
G.-Q. Zhang, L. Cui, R. Mueller, S. Tao, M. Kim, M. Rueschman, S. Mariani, D. Mobley, S. Redline, The national sleep research resource: towards a sleep data commons, Journal of the American Medical Informatics Association 25 (10) (2018) 1351–1358
2018
Cited alongside, same era.
Y. LeCun, A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27, Open Review 62 (2022)
2022
Later among the works it cites.
P. An, J. Zhao, B. Du, W. Zhao, T. Zhang, Z. Yuan, Amplitude–time dual-view fused eeg temporal feature learning for automatic sleep staging, IEEE Transactions on Neural Networks and Learning Systems (2022)
2022
Later among the works it cites.
H. Phan, K. Mikkelsen, O. Y. Chén, P. Koch, A. Mertins, M. De Vos, Sleeptransformer: Automatic sleep staging with interpretability and uncertainty quantification, IEEE Transactions on Biomedical Engineering 69 (8) (2022) 2456–2467
2022
Later among the works it cites.
L. Fiorillo, G. Monachino, J. van der Meer, M. Pesce, J. D. Warncke, M. H. Schmidt, C. L. Bassetti, A. Tzovara, P. Favaro, F. D. Faraci, U-sleep’s resilience to aasm guidelines, NPJ digital medicine 6 (1) (2023) 33
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. K. Pavlova, V. Latreille, Sleep disorders, The American journal of medicine 132 (3) (2019) 292–299
2019
Cited alongside, same era.
H. Phan, F. Andreotti, N. Cooray, O. Y. Chén, M. De Vos, Seqsleepnet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging, IEEE Transactions on Neural Systems and Rehabilitation Engineering 27 (3) (2019) 400–410
2019
Cited alongside, same era.
D.-M. Ross, E. Cretu, Probabilistic modelling of sleep stage and apneaic events in the university college of dublin database (ucddb), in: 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), IEEE, 2019, pp. 0133–0139
2019
Cited alongside, same era.
H. Seo, S. Back, S. Lee, D. Park, T. Kim, K. Lee, Intra-and inter-epoch temporal context network (iitnet) using sub-epoch features for automatic sleep scoring on raw single-channel eeg, Biomedical signal processing and control 61 (2020) 102037
2020
Cited alongside, same era.
A. Baevski, Y. Zhou, A. Mohamed, M. Auli, wav2vec 2.0: A framework for self-supervised learning of speech representations, Advances in neural information processing systems 33 (2020) 12449–12460
2020
Cited alongside, same era.
H. Phan, O. Y. Chén, M. C. Tran, P. Koch, A. Mertins, M. De Vos, Xsleepnet: Multi-view sequential model for automatic sleep staging, IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (9) (2021) 5903–5915
2021
Cited alongside, same era.
M. M. Bronstein, J. Bruna, T. Cohen, P. Veličković, Geometric deep learning: Grids, groups, graphs, geodesics, and gauges, arXiv preprint 2104.13478 (2021) · 2021
Cited alongside, same era.
doi:10.1038/s41746-021-00440-5
M. Perslev, S. Darkner, L. Kempfner, M. Nikolic, P. J. Jennum, C. Igel, U-sleep: resilient high-frequency sleep staging, npj Digital Medicine 4 (1) (Apr. 2021) · 2021
Cited alongside, same era.
2023
Closest in time.
W. Zhang, L. Yang, S. Geng, S. Hong, Self-supervised time series representation learning via cross reconstruction transformer, IEEE Transactions on Neural Networks and Learning Systems (2024) 1–10 doi:10.1109/tnnls.2023.3292066
2023
Closest in time.
V. Gorade, A. Singh, D. Mishra, Large scale time-series representation learning via simultaneous low-and high-frequency feature bootstrapping, IEEE Transactions on Neural Networks and Learning Systems (2023)
2023
Closest in time.
H. Phan, K. P. Lorenzen, E. Heremans, O. Y. Chén, M. C. Tran, P. Koch, A. Mertins, M. Baumert, K. B. Mikkelsen, M. D. Vos, L-SeqSleepNet: Whole-cycle long sequence modelling for automatic sleep staging, IEEE Journal of Biomedical and Health Informatics (2023) 1–10 doi:10.1109/jbhi.2023.3303197
2023
Closest in time.
A. Einizade, S. Nasiri, S. H. Sardouie, G. D. Clifford, Productgraphsleepnet: Sleep staging using product spatio-temporal graph learning with attentive temporal aggregation, Neural Networks 164 (2023) 667–680
2023
Closest in time.
Z. Jin, K. Jia, Sagsleepnet: A deep learning model for sleep staging based on self-attention graph of polysomnography, Biomedical Signal Processing and Control 86 (2023) 105062
2023
Closest in time.
A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, I. Sutskever, Robust speech recognition via large-scale weak supervision, in: International Conference on Machine Learning, PMLR, 2023, pp. 28492–28518
2023
Closest in time.
W. Pei, Y. Li, P. Wen, F. Yang, X. Ji, An automatic method using mfcc features for sleep stage classification, Brain Informatics 11 (1) (2024) 6
2024
Closest in time.
T. Wang, N. Strodthoff, Assessing the importance of long-range correlations for deep-learning-based sleep staging, in: Workshop Biosignalsm Göttingen, Germany (non-archival), 2024 · 2024
Closest in time.
R. Bhirangi, C. Wang, V. Pattabiraman, C. Majidi, A. Gupta, T. Hellebrekers, L. Pinto, Hierarchical state space models for continuous sequence-to-sequence modeling, arXiv preprint 2402.10211 (2024) · 2024
Closest in time.
T. Wang, N. Strodthoff, Source code for: S4Sleep: Elucidating the design space of deep-learning-based long time series classification models- a case study based on sleep staging, github.com https://github.com/AI4HealthUOL/s4sleep (Jan. 2025)
2025
Closest in time.