Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.
Artificial intelligence
Disorders of excessive somnolence
Machine learning
Polysomnogram
Sleep apnea
Journal
Sleep & breathing = Schlaf & Atmung
ISSN: 1522-1709
Titre abrégé: Sleep Breath
Pays: Germany
ID NLM: 9804161
Informations de publication
Date de publication:
03 2023
03 2023
Historique:
received:
22
11
2021
accepted:
02
03
2022
revised:
25
01
2022
pubmed:
10
3
2022
medline:
10
3
2023
entrez:
9
3
2022
Statut:
ppublish
Résumé
The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.
Sections du résumé
BACKGROUND
The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI.
METHOD
The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature.
RESULTS
Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice.
CONCLUSION
Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.
Identifiants
pubmed: 35262853
doi: 10.1007/s11325-022-02592-4
pii: 10.1007/s11325-022-02592-4
pmc: PMC8904207
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
39-55Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Références
Watson NF, Fernandez CR (2021) Artificial intelligence and sleep advancing sleep medicine. Sleep Med Rev 59:101512
pubmed: 34166990
doi: 10.1016/j.smrv.2021.101512
Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S et al (2020) Artificial intelligence in sleep medicine: background and implications for clinicians. J Clin Sleep Med 16(4):609–618
pubmed: 32065113
pmcid: 7161463
doi: 10.5664/jcsm.8388
Malhotra A, Ayappa I, Ayas N, Collop N, Kirsch D, McArdle N, et al. Metrics of sleep apnea severity: beyond the apnea-hypopnea index. Sleep. 2021;44(7).
Hartmann S, Bruni O, Ferri R, Redline S, Baumert M. Characterization of cyclic alternating pattern during sleep in older men and women using large population studies. Sleep. 2020;43(7).
Jonasdottir SS, Minor K, Lehmann S. Gender differences in nighttime sleep patterns and variability across the adult lifespan: a global-scale wearables study. Sleep. 2021;44(2).
Sun H, Ganglberger W, Panneerselvam E, Leone MJ, Quadri SA, Goparaju B, et al. Sleep staging from electrocardiography and respiration with deep learning. Sleep. 2020;43(7).
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).
Korkalainen H, Aakko J, Duce B, Kainulainen S, Leino A, Nikkonen S, et al. Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea. Sleep. 2020;43(11).
Huang WC, Lee PL, Liu YT, Chiang AA, Lai F. Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample. Sleep. 2020;43(7).
Lechat B, Hansen K, Catcheside P, Zajamsek B. Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning. Sleep. 2020;43(10).
Wickwire EM, Jobe SL, Oldstone LM, Scharf SM, Johnson AM, Albrecht JS. Lower socioeconomic status and co-morbid conditions are associated with reduced continuous positive airway pressure adherence among older adult medicare beneficiaries with obstructive sleep apnea. Sleep. 2020;43(12).
Finnsson E, Ólafsdóttir GH, Loftsdóttir DL, Jónsson S, Helgadóttir H, Ágústsson JS, et al. A scalable method of determining physiological endotypes of sleep apnea from a polysomnographic sleep study. Sleep. 2021;44(1).
Williamson AA, Zendarski N, Lange K, Quach J, Molloy C, Clifford SA, et al. Sleep problems, internalizing and externalizing symptoms, and domains of health-related quality of life: bidirectional associations from early childhood to early adolescence. Sleep. 2021;44(1).
Redline S, Purcell SM. Sleep and Big Data: harnessing data, technology, and analytics for monitoring sleep and improving diagnostics, prediction, and interventions-an era for Sleep-Omics? Sleep. 2021;44(6).
Moe S, Rustad AM, Hanssen KG, editors. Machine learning in control systems: an overview of the state of the art. International Conference on Innovative Techniques and Applications of Artificial Intelligence; 2018: Springer.
Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A (2018) Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Sci Rep 8(1):1–10
doi: 10.1038/s41598-018-26174-1
Ayyadevara VK (2018) Basics of machine learning. Springer, Pro Machine Learning Algorithms, pp 1–15
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).
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circ 101(23):e215–e20
doi: 10.1161/01.CIR.101.23.e215
O’reilly C, Gosselin N, Carrier J, Nielsen T (2014) Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research. J Sleep Res 23(6):628–635
pubmed: 24909981
doi: 10.1111/jsr.12169
Quan SF, Howard BV, Iber C, Kiley JP, Nieto FJ, O’Connor GT et al (1997) The sleep heart health study: design, rationale, and methods. Sleep 20(12):1077–1085
pubmed: 9493915
Kubat M, Pfurtscheller G, Flotzinger D (1994) AI-based approach to automatic sleep classification. Biol Cybern 70(5):443–448
pubmed: 8186305
doi: 10.1007/BF00203237
Schaltenbrand N, Lengelle R, Toussaint M, Luthringer R, Carelli G, Jacqmin A et al (1996) Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients. Sleep 19(1):26–35
pubmed: 8650459
doi: 10.1093/sleep/19.1.26
El-Solh AA, Mador MJ, Ten-Brock E, Shucard DW, Abul-Khoudoud M, Grant BJ (1999) Validity of neural network in sleep apnea. Sleep. 22(1):105–11
pubmed: 9989371
doi: 10.1093/sleep/22.1.105
Espie CA, Paul A, McFie J, Amos P, Hamilton D, McColl JH et al (1998) Sleep studies of adults with severe or profound mental retardation and epilepsy. Am J Ment Retard 103(1):47–59
pubmed: 9678230
doi: 10.1352/0895-8017(1998)103<0047:SSOAWS>2.0.CO;2
El-Solh AA, Magalang UJ, Mador MJ, Dmochowski J, Veeramachaneni S, Saberi A et al (2003) The utility of neural network in the diagnosis of Cheyne-Stokes respiration. J Med Eng Technol 27(2):54–58
pubmed: 12745912
doi: 10.1080/0309190021000043693
Almazaydeh L, Elleithy K, Faezipour M (2012) Obstructive sleep apnea detection using SVM-based classification of ECG signal features. Annu Int Conf IEEE Eng Med Biol Soc 2012:4938–4941
pubmed: 23367035
Christensen JA, Zoetmulder M, Koch H, Frandsen R, Arvastson L, Christensen SR et al (2014) Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson’s and Parkinson’s disease. J Neurosci Methods 235:262–276
pubmed: 25088694
doi: 10.1016/j.jneumeth.2014.07.014
Huupponen E, Saastamoinen A, Niemi J, Virkkala J, Hasan J, Värri A et al (2005) Automated frequency analysis of synchronous and diffuse sleep spindles. Neuropsychobiology 51(4):256–264
pubmed: 15905631
doi: 10.1159/000085821
Redmond SJ, Heneghan C (2006) Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea. IEEE Trans Biomed Eng 53(3):485–496
pubmed: 16532775
doi: 10.1109/TBME.2005.869773
Suhas SR, Behbehani K, Vijendra S, Burk JR, Lucas EA (2007) ECG biomarkers for simultaneous detection of obstructive sleep apnea and Cheyne-Stokes breathing. Annu Int Conf IEEE Eng Med Biol Soc 2007:1047–1050
pubmed: 18002140
Lewicke A, Sazonov E, Corwin MJ, Neuman M, Schuckers S (2008) Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models. IEEE Trans Biomed Eng 55(1):108–118
pubmed: 18232352
doi: 10.1109/TBME.2007.900558
Azarbarzin A, Moussavi ZM (2011) Automatic and unsupervised snore sound extraction from respiratory sound signals. IEEE Trans Biomed Eng 58(5):1156–1162
pubmed: 20679022
doi: 10.1109/TBME.2010.2061846
Willemen T, Van Deun D, Verhaert V, Vandekerckhove M, Exadaktylos V, Verbraecken J et al (2014) An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification. IEEE J Biomed Health Inform 18(2):661–669
pubmed: 24058031
doi: 10.1109/JBHI.2013.2276083
Burke MJ, Downes R (2006) A fuzzy logic based apnoea monitor for SIDS risk infants. J Med Eng Technol 30(6):397–411
pubmed: 17060168
doi: 10.1080/03091900600590140
Gerla V, Paul K, Lhotska L, Krajca V (2009) Multivariate analysis of full-term neonatal polysomnographic data. IEEE Trans Inf Technol Biomed 13(1):104–110
pubmed: 19129029
doi: 10.1109/TITB.2008.2007193
Fontenla-Romero O, Guijarro-Berdiñas B, Alonso-Betanzos A, Moret-Bonillo V (2005) A new method for sleep apnea classification using wavelets and feedforward neural networks. Artif Intell Med 34(1):65–76
pubmed: 15885567
doi: 10.1016/j.artmed.2004.07.014
Otero A, Felix P, Alvarez MR, Zamarron C (2008) Fuzzy structural algorithms to identify and characterize apnea and hypopnea episodes. Annu Int Conf IEEE Eng Med Biol Soc 2008:5242–5245
pubmed: 19163899
Dimitriadis SI, Salis C, Linden D (2018) A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates. Clin Neurophysiol 129(4):815–828
pubmed: 29477981
doi: 10.1016/j.clinph.2017.12.039
Procházka A, Kuchyňka J, Vyšata O, Cejnar P, Vališ M, Mařík V (2018) Multi-class sleep stage analysis and adaptive pattern recognition. Appl Sci 8(5):697
doi: 10.3390/app8050697
Rahman MM, Bhuiyan MIH, Hassan AR (2018) Sleep stage classification using single-channel EOG. Comput Biol Med 102:211–220
pubmed: 30170769
doi: 10.1016/j.compbiomed.2018.08.022
Tsinalis O, Matthews PM, Guo Y, Zafeiriou S. Automatic sleep stage scoring with single-channel EEG using convolutional neural networks. arXiv preprint arXiv:161001683. 2016.
Zhang L, Fabbri D, Upender R, Kent D (2019) Automated sleep stage scoring of the sleep heart health study using deep neural networks. Sleep. 42(11):zsz159
pubmed: 31289828
pmcid: 6802563
doi: 10.1093/sleep/zsz159
Chriskos P, Frantzidis CA, Nday CM, Gkivogkli PT, Bamidis PD (2021) Kourtidou-Papadeli C. A review on current trends in automatic sleep staging through bio-signal recordings and future challenges. Sleep medicine reviews. 55:101377
pubmed: 33017770
doi: 10.1016/j.smrv.2020.101377
Fiorillo L, Puiatti A, Papandrea M, Ratti P-L, Favaro P, Roth C et al (2019) Automated sleep scoring: a review of the latest approaches. Sleep medicine reviews. 48:101204
pubmed: 31491655
doi: 10.1016/j.smrv.2019.07.007
Cui Z, Zheng X, Shao X, Cui L. Automatic sleep stage classification based on convolutional neural network and fine-grained segments. Complexity. 2018;2018.
Erdenebayar U, Kim YJ, Park J-U, Joo EY, Lee K-J (2019) Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram. Comput Methods Programs Biomed 180:105001
pubmed: 31421606
doi: 10.1016/j.cmpb.2019.105001
Calderón JM, Álvarez-Pitti J, Cuenca I, Ponce F, Redon P (2020) Development of a minimally invasive screening tool to identify obese pediatric population at risk of obstructive sleep apnea/hypopnea syndrome. Bioeng 7(4):131
ElMoaqet H, Eid M, Glos M, Ryalat M, Penzel T (2020) Deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals. Sensors 20(18):5037
pubmed: 32899819
pmcid: 7570636
doi: 10.3390/s20185037
Biswal S, Sun H, Goparaju B, Westover MB, Sun J, Bianchi MT (2018) Expert-level sleep scoring with deep neural networks. J Am Med Inform Assoc 25(12):1643–1650
pubmed: 30445569
pmcid: 6289549
doi: 10.1093/jamia/ocy131
Zhang J, Wu Y (2017) A new method for automatic sleep stage classification. IEEE Trans Biomed Circuits Syst 11(5):1097–1110
pubmed: 28809709
doi: 10.1109/TBCAS.2017.2719631
Haidar R, McCloskey S, Koprinska I, Jeffries B, editors. Convolutional neural networks on multiple respiratory channels to detect hypopnea and obstructive apnea events. 2018 International Joint Conference on Neural Networks (IJCNN); 2018: IEEE.
Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus C, Vaughn BV (2012) The AASM manual for the scoring of sleep and associated events Rules, Terminology and Technical Specifications, Darien, Illinois. Am Acad Sleep Med 176:2012
Banluesombatkul N, Rakthanmanon T, Wilaiprasitporn T, editors. Single channel ECG for obstructive sleep apnea severity detection using a deep learning approach. TENCON 2018–2018 IEEE Region 10 Conference; 2018: IEEE.
Supratak A, Dong H, Wu C, Guo Y (2017) DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng 25(11):1998–2008
pubmed: 28678710
doi: 10.1109/TNSRE.2017.2721116
Malafeev A, Laptev D, Bauer S, Omlin X, Wierzbicka A, Wichniak A et al (2018) Automatic human sleep stage scoring using deep neural networks. Front Neurosci 12:781
pubmed: 30459544
pmcid: 6232272
doi: 10.3389/fnins.2018.00781
Stephansen JB, Olesen AN, Olsen M, Ambati A, Leary EB, Moore HE et al (2018) Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nat Commun 9(1):1–15
doi: 10.1038/s41467-018-07229-3
Edwards BA, Redline S, Sands SA, Owens RL (2019) More than the sum of the respiratory events: personalized medicine approaches for obstructive sleep apnea. Am J Respir Crit Care Med 200(6):691–703
pubmed: 31022356
pmcid: 6775874
doi: 10.1164/rccm.201901-0014TR
Miaskowski C, Barsevick A, Berger A, Casagrande R, Grady PA, Jacobsen P, et al. Advancing symptom science through symptom cluster research: expert panel proceedings and recommendations. JNCI: Journal of the National Cancer Institute. 2017;109(4).
Dutta R, Delaney G, Toson B, Jordan AS, White DP, Wellman A et al (2021) A novel model to estimate key obstructive sleep apnea endotypes from standard polysomnography and clinical data and their contribution to obstructive sleep apnea severity. Ann Am Thorac Soc 18(4):656–667
pubmed: 33064953
pmcid: 8008997
doi: 10.1513/AnnalsATS.202001-064OC
Ma E-Y, Kim J-W, Lee Y, Cho S-W, Kim H, Kim JK (2021) Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea. Sci Rep 11(1):1–15
Jensen JB, Sorensen HB, Kempfner J, Sørensen GL, Knudsen S, Jennum P (2014) Sleep-Wake transition in narcolepsy and healthy controls using a support vector machine. J Clin Neurophysiol 31(5):397–401
pubmed: 25271675
doi: 10.1097/WNP.0000000000000074
Olsen AV, Stephansen J, Leary E, Peppard PE, Sheungshul H, Jennum PJ et al (2017) Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy. J Neurosci Methods 282:9–19
pubmed: 28219726
doi: 10.1016/j.jneumeth.2017.02.004
Trotti LM (2020) Twice is nice? Test-retest reliability of the Multiple Sleep Latency Test in the central disorders of hypersomnolence. J Clin Sleep Med 16(S1):17–18
pubmed: 33054965
pmcid: 7792901
doi: 10.5664/jcsm.8884
Zhang Z, Mayer G, Dauvilliers Y, Plazzi G, Pizza F, Fronczek R et al (2018) Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning. Sci Rep 8(1):1–11
Liu D, Pang Z, Lloyd SR (2008) A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG. IEEE Trans Neural Networks 19(2):308–318
pubmed: 18269961
doi: 10.1109/TNN.2007.908634
Aydın S, Saraoǧlu HM, Kara S (2011) Singular spectrum analysis of sleep EEG in insomnia. J Med Syst 35(4):457–461
pubmed: 20703545
doi: 10.1007/s10916-009-9381-7
Chaparro-Vargas R, Ahmed B, Wessel N, Penzel T, Cvetkovic D (2016) Insomnia characterization: from hypnogram to graph spectral theory. IEEE Trans Biomed Eng 63(10):2211–2219
pubmed: 26742123
doi: 10.1109/TBME.2016.2515261
Shahin M, Ahmed B, Hamida ST, Mulaffer FL, Glos M, Penzel T (2017) Deep learning and insomnia: assisting clinicians with their diagnosis. IEEE J Biomed Health Inform 21(6):1546–1553
pubmed: 28092583
doi: 10.1109/JBHI.2017.2650199
Doan S, Yang EW, Tilak SS, Li PW, Zisook DS, Torii M (2019) Extracting health-related causality from twitter messages using natural language processing. BMC Med Inform Decis Mak 19(Suppl 3):79
pubmed: 30943954
pmcid: 6448183
doi: 10.1186/s12911-019-0785-0
Park S, Lee SW, Han S, Cha M (2019) Clustering insomnia patterns by data from wearable devices: algorithm development and validation study. JMIR Mhealth Uhealth 7(12):e14473
pubmed: 31804187
pmcid: 6923760
doi: 10.2196/14473
Philip P, Dupuy L, Morin CM, de Sevin E, Bioulac S, Taillard J et al (2020) Smartphone-based virtual agents to help individuals with sleep concerns during COVID-19 confinement feasibility study. J Med Internet Res. 22(12):e24268
pubmed: 33264099
pmcid: 7752183
doi: 10.2196/24268
Esen H, Hatipoğlu T, Cihan A, Fiğlali N (2019) Expert system application for prioritizing preventive actions for shift work: shift expert. Int J Occup Saf Ergon 25(1):123–137
pubmed: 28675084
doi: 10.1080/10803548.2017.1350392
Anafi RC, Francey LJ, Hogenesch JB, Kim J (2017) CYCLOPS reveals human transcriptional rhythms in health and disease. Proc Natl Acad Sci U S A 114(20):5312–5317
pubmed: 28439010
pmcid: 5441789
doi: 10.1073/pnas.1619320114
Braun R, Kath WL, Iwanaszko M, Kula-Eversole E, Abbott SM, Reid KJ et al (2018) Universal method for robust detection of circadian state from gene expression. Proc Natl Acad Sci U S A 115(39):E9247–E9256
pubmed: 30201705
pmcid: 6166804
doi: 10.1073/pnas.1800314115
Hesse J, Malhan D, Yalҫin M, Aboumanify O, Basti A, Relógio A. An optimal time for treatment-predicting circadian time by machine learning and mathematical modelling. Cancers (Basel). 2020;12(11).
Huang Y, Mayer C, Cheng P, Siddula A, Burgess HJ, Drake C, et al. Predicting circadian phase across populations: a comparison of mathematical models and wearable devices. Sleep. 2021.
Högl B, Santamaria J, Iranzo A, Stefani A (2019) Precision medicine in rapid eye movement sleep behavior disorder. Sleep Med Clin 14(3):351–362
pubmed: 31375203
doi: 10.1016/j.jsmc.2019.04.003
Cooray N, Andreotti F, Lo C, Symmonds M, Hu MT, De Vos M (2019) Detection of REM sleep behaviour disorder by automated polysomnography analysis. Clin Neurophysiol 130(4):505–514
pubmed: 30772763
doi: 10.1016/j.clinph.2019.01.011
Prashanth R, Roy SD, Mandal PK, Ghosh S (2016) High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning. Int J Med Informatics 90:13–21
doi: 10.1016/j.ijmedinf.2016.03.001
Umut I, Çentik G. Detection of periodic leg movements by machine learning methods using polysomnographic parameters other than leg electromyography. Computational and mathematical methods in medicine. 2016;2016.
Veauthier C, Ryczewski J, Mansow-Model S, Otte K, Kayser B, Glos M et al (2019) Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing. Sci Rep 9(1):16812
pubmed: 31727918
pmcid: 6856090
doi: 10.1038/s41598-019-53050-3
Zhou P, Huang L, Zhao Q, Xiao W, Li S (2019) A domestic diagnosis system for early restless legs syndrome based on deep learning. Zhongguo Yi Liao Qi Xie Za Zhi 43(2):79–82
pubmed: 30977599
Buysse DJ (2014) Sleep health: can we define it? Does it matter? Sleep 37(1):9–17
pubmed: 24470692
pmcid: 3902880
doi: 10.5665/sleep.3298
Wallace ML, Stone K, Smagula SF, Hall MH, Simsek B, Kado DM, et al. Which sleep health characteristics predict all-cause mortality in older Men? An application of flexible multivariable approaches. Sleep. 2018;41(1).
van Gilst MM, van Dijk JP, Krijn R, Hoondert B, Fonseca P, van Sloun RJG et al (2019) Protocol of the SOMNIA project an observational study to create a neurophysiological database for advanced clinical sleep monitoring. BMJ Open. 9(11):e030996
pubmed: 31772091
pmcid: 6886950
doi: 10.1136/bmjopen-2019-030996
Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C (2021) U-Sleep: resilient high-frequency sleep staging. NPJ Digit Med 4(1):72
pubmed: 33859353
pmcid: 8050216
doi: 10.1038/s41746-021-00440-5
Sharma M, Tiwari J, Acharya UR. Automatic sleep-stage scoring in healthy and sleep disorder patients using optimal wavelet filter bank technique with EEG signals. Int J Environ Res Public Health. 2021;18(6).
Abou Jaoude M, Sun H, Pellerin KR, Pavlova M, Sarkis RA, Cash SS, et al. Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning. Sleep. 2020;43(11).
Zhang X, Xu M, Li Y, Su M, Xu Z, Wang C et al (2020) Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data. Sleep Breath 24(2):581–590
pubmed: 31938990
pmcid: 7289784
doi: 10.1007/s11325-019-02008-w
Peter-Derex L, Berthomier C, Taillard J, Berthomier P, Bouet R, Mattout J et al (2021) Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders. J Clin Sleep Med 17(3):393–402
pubmed: 33089777
pmcid: 7927318
doi: 10.5664/jcsm.8864
Sridhar N, Shoeb A, Stephens P, Kharbouch A, Shimol DB, Burkart J et al (2020) Erratum: Author Correction: Deep learning for automated sleep staging using instantaneous heart rate. NPJ Digit Med 3:131
pubmed: 33083566
pmcid: 7546610
doi: 10.1038/s41746-020-00337-9
Zhu T, Luo W, Yu F. Convolution-and attention-based neural network for automated sleep stage classification. Int J Environ Res Public Health. 2020;17(11).
Xu Z, Yang X, Sun J, Liu P, Qin W. Sleep stage classification using time-frequency spectra from consecutive multi-time points. Frontiers in Neuroscience. 2020;14(14).
Zhang L, Fabbri D, Upender R, Kent D. Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks. Sleep. 2019;42(11).
Yildirim O, Baloglu UB, Acharya UR. A deep learning model for automated sleep stages classification using PSG signals. Int J Environ Res Public Health. 2019;16(4).
Phan H, Andreotti F, Cooray N, Chen OY, De Vos M (2019) SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Trans Neural Syst Rehabil Eng 27(3):400–410
pubmed: 30716040
pmcid: 6481557
doi: 10.1109/TNSRE.2019.2896659
Zhang J, Wu Y (2018) Complex-valued unsupervised convolutional neural networks for sleep stage classification. Comput Methods Programs Biomed 164:181–191
pubmed: 30195426
doi: 10.1016/j.cmpb.2018.07.015
Sors A, Bonnet S, Mirek S, Vercueil L, Payen J-F (2018) A convolutional neural network for sleep stage scoring from raw single-channel EEG. Biomed Signal Process Control 42:107–114
doi: 10.1016/j.bspc.2017.12.001
Chambon S, Galtier MN, Arnal PJ, Wainrib G, Gramfort A (2018) A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans Neural Syst Rehabil Eng 26(4):758–769
pubmed: 29641380
doi: 10.1109/TNSRE.2018.2813138
Vilamala A, Madsen KH, Hansen LK, editors. Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP); 2017 25–28 Sept. 2017.