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Original Research

Sleep Stage Classification Based on Multi-Centers: Comparison Between Different Ages, Mental Health Conditions and Acquisition Devices

ORCID Icon, ORCID Icon, , , &
Pages 995-1007 | Published online: 24 May 2022

References

  • Cirelli C, Tononi G. Is sleep essential? PLoS Biol. 2008;6(8):e216. doi:10.1371/journal.pbio.0060216
  • Geiker NRW, Astrup A, Hjorth MF, Sjödin A, Pijls L, Markus CR. Does stress influence sleep patterns, food intake, weight gain, abdominal obesity and weight loss interventions and vice versa? Obes Rev. 2018;19:81–97. doi:10.1111/obr.12603
  • Iranzo A. Sleep and neurological autoimmune diseases. Neuropsychopharmacology. 2020;45(1):129–140. doi:10.1038/s41386-019-0463-z
  • Berry RB, Albertario CL, Harding SM, et al. for the American Academy of Sleep Medicine. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Darien, IL: American Academy of Sleep Medicine. 2018.
  • Furrer M, Jaramillo V, Volk C, et al. Sleep EEG slow-wave activity in medicated and unmedicated children and adolescents with attention-deficit/hyperactivity disorder. Transl Psychiatry. 2019;9(1):324. doi:10.1038/s41398-019-0659-3
  • Diep C, Garcia-Molina G, Jasko J, et al. Acoustic enhancement of slow wave sleep on consecutive nights improves alertness and attention in chronically short sleepers. Sleep Med. 2021;81:69–79. doi:10.1016/j.sleep.2021.01.044
  • Ferrarelli F, Kaskie R, Laxminarayan S, et al. An increase in sleep slow waves predicts better working memory performance in healthy individual. Neuroimage. 2019;191:1–9. doi:10.1016/j.neuroimage.2019.02.020
  • Fernandez LMJ, Lüthi A. Sleep spindles: mechanisms and functions. Physiol Rev. 2020;100(2):805–868. doi:10.1152/physrev.00042.2018
  • Pushpanathan ME, Loftus AM, Thomas MG, et al. The relationship between sleep and cognition in Parkinson’s disease: a meta-analysis. Sleep Med Rev. 2016;26:21–32. doi:10.1016/j.smrv.2015.04.003
  • Zhang F, Zhong R, Li S, et al. Alteration in sleep architecture and electroencephalogram as an early sign of Alzheimer’s disease preceding the disease pathology and cognitive decline. Alzheimers Dement. 2019;15(4):590–597. doi:10.1016/j.jalz.2018.12.004
  • Bartsch U, Simpkin AJ, Demanuele C, et al. Distributed slow-wave dynamics during sleep predict memory consolidation and its impairment in schizophrenia. NPJ Schizophr. 2019;5(1):18. doi:10.1038/s41537-019-0086-8
  • Markovic A, Buckley A, Driver DI, et al. Sleep spindle activity in childhood onset schizophrenia: diminished and associated with clinical symptoms. Schizophr Res. 2020;223:327–336. doi:10.1016/j.schres.2020.08.022
  • Boostani R, Karimzadeh F, Nami M. A comparative review on sleep stage classification methods in patients and healthy individuals. Comput Methods Programs Biomed. 2017;140:77–91. doi:10.1016/j.cmpb.2016.12.004
  • Fonseca P, Long X, Radha M, Haakma R, Aarts RM, Rolink J. Sleep stage classification with ECG and respiratory effort. Physiol Meas. 2015;36(10):2027–2040. doi:10.1088/0967-3334/36/10/2027
  • Shi J, Liu X, Li Y, Zhang Q, Li Y, Ying S. Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning. J Neurosci Methods. 2015;254:94–101. doi:10.1016/j.jneumeth.2015.07.006
  • Sousa T, Cruz A, Khalighi S, Pires G, Nunes U. A two-step automatic sleep stage classification method with dubious range detection. Comput Biol Med. 2015;59:42–53. doi:10.1016/j.compbiomed.2015.01.017
  • Biswal S, Kulas J, Sun H, et al. SLEEPNET: automated sleep staging system via deep learning. arXvi. 2017. doi:10.48550/arxiv.1707.08262
  • Sun C, Chen C, Li W, Fan J, Chen W. A hierarchical neural network for sleep stage classification based on comprehensive feature learning and multi-flow sequence learning. IEEE J Biomed Health Inform. 2020;24(5):1351–1366. doi:10.1109/JBHI.2019.2937558
  • Xu ZL, Yang XJ, Sun JB, Liu P, Qin W. Sleep stage classification using time-frequency spectra from consecutive multi-time points. Front Neurosci. 2020;14:14. doi:10.3389/fnins.2020.00014
  • Patanaik A, Ong JL, Gooley JJ, Ancoli-Israel S, Chee MWL. An end-to-end framework for real-time automatic sleep stage classification. Sleep. 2018;41(5):1–11. doi:10.1093/sleep/zsy041
  • Stephansen JB, Olesen AN, Olsen M, et al. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nat Commun. 2018;9(1):5229. doi:10.1038/s41467-018-07229-3
  • Biswal S, Sun H, Goparaju B, Westover MB, Sun J, Bianchi MT. Expert-level sleep scoring with deep neural networks. J Am Med Informatics Assoc. 2018;25(12):1643–1650. doi:10.1093/jamia/ocy131
  • Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C. U-sleep: resilient high-frequency sleep staging. NPJ Digit Med. 2021;4(1):72. doi:10.1038/s41746-021-00440-5
  • Olesen AN, Jørgen Jennum P, Mignot E, Sorensen HBD. Automatic sleep stage classification with deep residual networks in a mixed-cohort setting. Sleep. 2021;44(1):zsaa161. doi:10.1093/sleep/zsaa161
  • Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–1780. doi:10.1162/neco.1997.9.8.1735
  • Zhang GQ, Cui L, Mueller R, et al. The National Sleep Research Resource: towards a sleep data commons. J Am Med Inform Assoc. 2018;25(10):1351–1358. doi:10.1093/jamia/ocy064
  • Quan SF, Howard BV, Iber C, et al. The sleep heart health study: design, rationale, and methods. Sleep. 1997;20(12):1077–1085.
  • Rosen CL, Larkin EK, Kirchner HL, et al. Prevalence and risk factors for sleep-disordered breathing in 8- to 11-year-old children: association with race and prematurity. J Pediatr. 2003;142(4):383–389. doi:10.1067/mpd.2003.28
  • Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90. doi:10.1145/3065386
  • Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv. 2015. doi:10.48550/arXiv.1409.1556
  • Szegedy C, Liu W, Jia YQ, et al. Going deeper with convolutions. arXiv. 2014. doi:10.48550/arXiv.1409.4842
  • He K, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. arXiv. 2015. doi:10.48550/arXiv.1512.03385
  • Xie S, Tu Z. Holistically-nested edge detection. Int J Comput Vis. 2017;125(1–3):3–18. doi:10.1007/s11263-017-1004-z
  • Tetko IV, Livingstone DJ, Luik AI. Neural network studies. 1. comparison of overfitting and overtraining. J Chem Inf Comput Sci. 1995;35(5):826–833. doi:10.1021/ci00027a006