BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Stephansen JB, Olesen AN, Olsen M, Ambati A, Leary EB, Moore HE, Carrillo O, Lin L, Han F, Yan H, Sun YL, Dauvilliers Y, Scholz S, Barateau L, Hogl B, Stefani A, Hong SC, Kim TW, Pizza F, Plazzi G, Vandi S, Antelmi E, Perrin D, Kuna ST, Schweitzer PK, Kushida C, Peppard PE, Sorensen HBD, Jennum P, Mignot E. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nat Commun 2018;9:5229. [PMID: 30523329 DOI: 10.1038/s41467-018-07229-3] [Cited by in Crossref: 78] [Cited by in F6Publishing: 55] [Article Influence: 19.5] [Reference Citation Analysis]
Number Citing Articles
1 Brink-kjaer A, Leary EB, Sun H, Westover MB, Stone KL, Peppard PE, Lane NE, Cawthon PM, Redline S, Jennum P, Sorensen HBD, Mignot E. Age estimation from sleep studies using deep learning predicts life expectancy. npj Digit Med 2022;5. [DOI: 10.1038/s41746-022-00630-9] [Reference Citation Analysis]
2 Horie K, Ota L, Miyamoto R, Abe T, Suzuki Y, Kawana F, Kokubo T, Yanagisawa M, Kitagawa H. Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability. Sci Rep 2022;12:12799. [PMID: 35896616 DOI: 10.1038/s41598-022-16334-9] [Reference Citation Analysis]
3 Lee H, Kim D, Park YL. Explainable Deep Learning Model for EMG-Based Finger Angle Estimation Using Attention. IEEE Trans Neural Syst Rehabil Eng 2022;30:1877-86. [PMID: 35834448 DOI: 10.1109/TNSRE.2022.3188275] [Reference Citation Analysis]
4 Decat N, Walter J, Koh ZH, Sribanditmongkol P, Fulcher BD, Windt JM, Andrillon T, Tsuchiya N. Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series. Sleep Med 2022;98:39-52. [PMID: 35779380 DOI: 10.1016/j.sleep.2022.06.013] [Reference Citation Analysis]
5 Barateau L, Lopez R, Chenini S, Rassu AL, Mouhli L, Dhalluin C, Jaussent I, Dauvilliers Y. Linking clinical complaints and objective measures of disrupted nighttime sleep in narcolepsy type 1. Sleep 2022;45:zsac054. [PMID: 35275598 DOI: 10.1093/sleep/zsac054] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
6 Barateau L, Pizza F, Plazzi G, Dauvilliers Y. 50th anniversary of the ESRS in 2022-JSR special issue. J Sleep Res 2022;:e13631. [PMID: 35624073 DOI: 10.1111/jsr.13631] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Xu Z, Zhu Y, Zhao H, Guo F, Wang H, Zheng M. Sleep Stage Classification Based on Multi-Centers: Comparison Between Different Ages, Mental Health Conditions and Acquisition Devices. NSS 2022;Volume 14:995-1007. [DOI: 10.2147/nss.s355702] [Reference Citation Analysis]
8 Radhakrishnan BL, Kirubakaran E, Jebadurai IJ, Selvakumar AI, Peter JD. Efficacy of Single-Channel EEG: A Propitious Approach for In-home Sleep Monitoring. Front Public Health 2022;10:839838. [DOI: 10.3389/fpubh.2022.839838] [Reference Citation Analysis]
9 Sharma M, Darji J, Thakrar M, Acharya UR. Automated identification of sleep disorders using wavelet-based features extracted from electrooculogram and electromyogram signals. Computers in Biology and Medicine 2022;143:105224. [DOI: 10.1016/j.compbiomed.2022.105224] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
10 Zhao C, Li J, Guo Y. SleepContextNet: A temporal context network for automatic sleep staging based single-channel EEG. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.106806] [Reference Citation Analysis]
11 Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath 2022. [PMID: 35262853 DOI: 10.1007/s11325-022-02592-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Kwon K, Kwon S, Yeo WH. Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes. Biosensors (Basel) 2022;12:155. [PMID: 35323425 DOI: 10.3390/bios12030155] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Lok R, Chawra D, Hon F, Ha M, Kaplan KA, Zeitzer JM. Objective underpinnings of self-reported sleep quality in middle-aged and older adults: the importance of N2 and wakefulness. Biological Psychology 2022. [DOI: 10.1016/j.biopsycho.2022.108290] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
14 Kim D, Lee J, Woo Y, Jeong J, Kim C, Kim D. Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification. JPM 2022;12:136. [DOI: 10.3390/jpm12020136] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
15 Cochen De Cock V, Dotov D, Lacombe S, Picot MC, Galtier F, Driss V, Giovanni C, Geny C, Abril B, Damm L, Janaqi S. Classifying Idiopathic Rapid Eye Movement Sleep Behavior Disorder, Controls, and Mild Parkinson's Disease Using Gait Parameters. Mov Disord 2022. [PMID: 35040193 DOI: 10.1002/mds.28894] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
16 Yang B, Wu W, Liu Y, Liu H. A Novel Sleep Stage Contextual Refinement Algorithm Leveraging Conditional Random Fields. IEEE Trans Instrum Meas 2022;71:1-13. [DOI: 10.1109/tim.2022.3154838] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Lovejoy CA, Abbas AR, Ratneswaran D. An introduction to artificial intelligence in sleep medicine. J Thorac Dis 2021;13:6095-8. [PMID: 34795955 DOI: 10.21037/jtd-21-1569] [Reference Citation Analysis]
18 Xu Z, Wang X, Zeng S, Ren X, Yan Y, Gong Z. Applying artificial intelligence for cancer immunotherapy. Acta Pharm Sin B 2021;11:3393-405. [PMID: 34900525 DOI: 10.1016/j.apsb.2021.02.007] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
19 Van der Plas D, Verbraecken J, Willemen M, Meert W, Davis J. Evaluation of Automated Hypnogram Analysis on Multi-Scored Polysomnographies. Front Digit Health 2021;3:707589. [PMID: 34713177 DOI: 10.3389/fdgth.2021.707589] [Reference Citation Analysis]
20 Vallat R, Walker MP. An open-source, high-performance tool for automated sleep staging. Elife 2021;10:e70092. [PMID: 34648426 DOI: 10.7554/eLife.70092] [Reference Citation Analysis]
21 Feng LX, Li X, Wang HY, Zheng WY, Zhang YQ, Gao DR, Wang MQ. Automatic Sleep Staging Algorithm Based on Time Attention Mechanism. Front Hum Neurosci 2021;15:692054. [PMID: 34483864 DOI: 10.3389/fnhum.2021.692054] [Reference Citation Analysis]
22 Martinot JB, Le-Dong NN, Cuthbert V, Denison S, Gozal D, Lavigne G, Pépin JL. Artificial Intelligence Analysis of Mandibular Movements Enables Accurate Detection of Phasic Sleep Bruxism in OSA Patients: A Pilot Study. Nat Sci Sleep 2021;13:1449-59. [PMID: 34466045 DOI: 10.2147/NSS.S320664] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
23 Penzel T, Glos M, Fietze I. New Trends and New Technologies in Sleep Medicine: Expanding Accessibility. Sleep Med Clin 2021;16:475-83. [PMID: 34325824 DOI: 10.1016/j.jsmc.2021.05.010] [Reference Citation Analysis]
24 Alvarez-Estevez D, Rijsman RM. Inter-database validation of a deep learning approach for automatic sleep scoring. PLoS One 2021;16:e0256111. [PMID: 34398931 DOI: 10.1371/journal.pone.0256111] [Reference Citation Analysis]
25 Maski KP, Colclasure A, Little E, Steinhart E, Scammell TE, Navidi W, Diniz Behn C. Stability of nocturnal wake and sleep stages defines central nervous system disorders of hypersomnolence. Sleep 2021;44:zsab021. [PMID: 33512510 DOI: 10.1093/sleep/zsab021] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Peter-Derex L, Berthomier C, Taillard J, Berthomier P, Bouet R, Mattout J, Brandewinder M, Bastuji H. Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders. J Clin Sleep Med 2021;17:393-402. [PMID: 33089777 DOI: 10.5664/jcsm.8864] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
27 Cesari M, Stefani A, Penzel T, Ibrahim A, Hackner H, Heidbreder A, Szentkirályi A, Stubbe B, Völzke H, Berger K, Högl B. 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. J Clin Sleep Med 2021;17:1237-47. [PMID: 33599203 DOI: 10.5664/jcsm.9174] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
28 Dong M. A Minireview on Temperature Dependent Protein Conformational Sampling. Protein J 2021;40:545-53. [PMID: 34181188 DOI: 10.1007/s10930-021-10012-x] [Reference Citation Analysis]
29 Wulterkens BM, Fonseca P, Hermans LWA, Ross M, Cerny A, Anderer P, Long X, van Dijk JP, Vandenbussche N, Pillen S, van Gilst MM, Overeem S. It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography. Nat Sci Sleep 2021;13:885-97. [PMID: 34234595 DOI: 10.2147/NSS.S306808] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
30 Pevernagie D. Future Treatment of Sleep Disorders: Syndromic Approach Versus Management of Treatable Traits? Sleep Med Clin 2021;16:465-73. [PMID: 34325823 DOI: 10.1016/j.jsmc.2021.05.005] [Reference Citation Analysis]
31 Sharma M, Tiwari J, Patel V, Acharya UR. Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals. Electronics 2021;10:1531. [DOI: 10.3390/electronics10131531] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
32 Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, Westover MB. Artificial intelligence in sleep medicine: background and implications for clinicians. J Clin Sleep Med 2020;16:609-18. [PMID: 32065113 DOI: 10.5664/jcsm.8388] [Cited by in Crossref: 19] [Cited by in F6Publishing: 10] [Article Influence: 19.0] [Reference Citation Analysis]
33 Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, Westover MB, Abbasi-Feinberg F, Aurora RN, Carden KA, Kirsch DB, Malhotra RK, Martin JL, Olson EJ, Ramar K, Rosen CL, Rowley JA, Shelgikar AV. Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement. J Clin Sleep Med 2020;16:605-7. [PMID: 32022674 DOI: 10.5664/jcsm.8288] [Cited by in Crossref: 17] [Cited by in F6Publishing: 10] [Article Influence: 17.0] [Reference Citation Analysis]
34 Watson NF, Fernandez CR. Artificial intelligence and sleep: Advancing sleep medicine. Sleep Med Rev 2021;59:101512. [PMID: 34166990 DOI: 10.1016/j.smrv.2021.101512] [Reference Citation Analysis]
35 Kam K, Rapoport DM, Parekh A, Ayappa I, Varga AW. WaveSleepNet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake. J Neurosci Methods 2021;360:109224. [PMID: 34052291 DOI: 10.1016/j.jneumeth.2021.109224] [Reference Citation Analysis]
36 Brink-Kjaer A, Christensen JAE, Cesari M, Mignot E, Sorensen HBD, Jennum P. Cortical arousal frequency is increased in narcolepsy type 1. Sleep 2021;44:zsaa255. [PMID: 33249455 DOI: 10.1093/sleep/zsaa255] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
37 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:zsaa161. [PMID: 32844179 DOI: 10.1093/sleep/zsaa161] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
38 Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C. U-Sleep: resilient high-frequency sleep staging. NPJ Digit Med 2021;4:72. [PMID: 33859353 DOI: 10.1038/s41746-021-00440-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Exarchos I, Rogers AA, Aiani LM, Gross RE, Clifford GD, Pedersen NP, Willie JT. Supervised and unsupervised machine learning for automated scoring of sleep-wake and cataplexy in a mouse model of narcolepsy. Sleep 2020;43:zsz272. [PMID: 31693157 DOI: 10.1093/sleep/zsz272] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
40 Gabel V, Miglis M, Zeitzer JM. Effect of artificial dawn light on cardiovascular function, alertness, and balance in middle-aged and older adults. Sleep 2020;43:zsaa082. [PMID: 32307533 DOI: 10.1093/sleep/zsaa082] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
41 Arnal PJ, Thorey V, Debellemaniere E, Ballard ME, Bou Hernandez A, Guillot A, Jourde H, Harris M, Guillard M, Van Beers P, Chennaoui M, Sauvet F. The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging. Sleep 2020;43:zsaa097. [PMID: 32433768 DOI: 10.1093/sleep/zsaa097] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 13.0] [Reference Citation Analysis]
42 Ambati A, Ju YE, Lin L, Olesen AN, Koch H, Hedou JJ, Leary EB, Sempere VP, Mignot E, Taheri S. Proteomic biomarkers of sleep apnea. Sleep 2020;43:zsaa086. [PMID: 32369590 DOI: 10.1093/sleep/zsaa086] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
43 Dietmann A, Wenz E, van der Meer J, Ringli M, Warncke JD, Edwards E, Schmidt MH, Bernasconi CA, Nirkko A, Strub M, Miano S, Manconi M, Acker J, von Manitius S, Baumann CR, Valko PO, Yilmaz B, Brunner AD, Tzovara A, Zhang Z, Largiadèr CR, Tafti M, Latorre D, Sallusto F, Khatami R, Bassetti CLA. The Swiss Primary Hypersomnolence and Narcolepsy Cohort study (SPHYNCS): Study protocol for a prospective, multicentre cohort observational study. J Sleep Res 2021;:e13296. [PMID: 33813771 DOI: 10.1111/jsr.13296] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
44 Malafeev A, Hertig-Godeschalk A, Schreier DR, Skorucak J, Mathis J, Achermann P. Automatic Detection of Microsleep Episodes With Deep Learning. Front Neurosci 2021;15:564098. [PMID: 33841068 DOI: 10.3389/fnins.2021.564098] [Reference Citation Analysis]
45 Krauss P, Metzner C, Joshi N, Schulze H, Traxdorf M, Maier A, Schilling A. Analysis and visualization of sleep stages based on deep neural networks. Neurobiol Sleep Circadian Rhythms 2021;10:100064. [PMID: 33763623 DOI: 10.1016/j.nbscr.2021.100064] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
46 Fu M, Wang Y, Chen Z, Li J, Xu F, Liu X, Hou F. Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography. Front Physiol 2021;12:628502. [PMID: 33746774 DOI: 10.3389/fphys.2021.628502] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
47 Martinot JB, Cuthbert V, Le-Dong NN, Coumans N, De Marneffe D, Letesson C, Pépin JL, Gozal D. Clinical validation of a mandibular movement signal based system for the diagnosis of pediatric sleep apnea. Pediatr Pulmonol 2021. [PMID: 33647188 DOI: 10.1002/ppul.25320] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
48 Hultman M, Johansson I, Lindqvist F, Ahlstrom C. Driver sleepiness detection with deep neural networks using electrophysiological data. Physiol Meas 2021. [PMID: 33621961 DOI: 10.1088/1361-6579/abe91e] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 Baier G, Zhang L, Wang Q, Moeller F. Extracting the transition network of epileptic seizure onset. Chaos 2021;31:023143. [DOI: 10.1063/5.0026074] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
50 Zhao X, Sun G. A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals. Entropy (Basel) 2021;23:E116. [PMID: 33477468 DOI: 10.3390/e23010116] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
51 Cesari M, Stefani A, Mitterling T, Frauscher B, Schönwald SV, Högl B. Sleep modelled as a continuous and dynamic process predicts healthy ageing better than traditional sleep scoring. Sleep Med 2021;77:136-46. [PMID: 33360558 DOI: 10.1016/j.sleep.2020.11.033] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
52 Kirsch DB. Disruption in Health Care (and Sleep Medicine): "It's the End of the World as We Know it…and I Feel Fine.". J Clin Sleep Med 2019;15:1185-8. [PMID: 31538585 DOI: 10.5664/jcsm.7900] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
53 Barateau L, Lopez R, Chenini S, Rassu A, Scholz S, Lotierzo M, Cristol J, Jaussent I, Dauvilliers Y. Association of CSF orexin-A levels and nocturnal sleep stability in patients with hypersomnolence. Neurology 2020;95:e2900-11. [DOI: 10.1212/wnl.0000000000010743] [Cited by in Crossref: 5] [Article Influence: 2.5] [Reference Citation Analysis]
54 Ryan S, Cummins EP, Farre R, Gileles-Hillel A, Jun JC, Oster H, Pepin JL, Ray DW, Reutrakul S, Sanchez-de-la-Torre M, Tamisier R, Almendros I. Understanding the pathophysiological mechanisms of cardiometabolic complications in obstructive sleep apnoea: towards personalised treatment approaches. Eur Respir J 2020;56:1902295. [PMID: 32265303 DOI: 10.1183/13993003.02295-2019] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
55 Skorucak J, Hertig-Godeschalk A, Schreier DR, Malafeev A, Mathis J, Achermann P. Automatic detection of microsleep episodes with feature-based machine learning. Sleep 2020;43:zsz225. [PMID: 31559424 DOI: 10.1093/sleep/zsz225] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
56 Phan H, Mikkelsen K, Chén OY, Koch P, Mertins A, Kidmose P, De Vos M. Personalized automatic sleep staging with single-night data: a pilot study with Kullback–Leibler divergence regularization. Physiol Meas 2020;41:064004. [DOI: 10.1088/1361-6579/ab921e] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
57 Zhai B, Perez-pozuelo I, Clifton EAD, Palotti J, Guan Y. Making Sense of Sleep: Multimodal Sleep Stage Classification in a Large, Diverse Population Using Movement and Cardiac Sensing. Proc ACM Interact Mob Wearable Ubiquitous Technol 2020;4:1-33. [DOI: 10.1145/3397325] [Cited by in Crossref: 11] [Cited by in F6Publishing: 1] [Article Influence: 5.5] [Reference Citation Analysis]
58 Svetnik V, Wang T, Xu Y, Hansen BJ, V. Fox S. A Deep Learning Approach for Automated Sleep-Wake Scoring in Pre-Clinical Animal Models. Journal of Neuroscience Methods 2020;337:108668. [DOI: 10.1016/j.jneumeth.2020.108668] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
59 Cesari M, Christensen JAE, Muntean ML, Mollenhauer B, Sixel-Döring F, Sorensen HBD, Trenkwalder C, Jennum P. A data-driven system to identify REM sleep behavior disorder and to predict its progression from the prodromal stage in Parkinson's disease. Sleep Med 2021;77:238-48. [PMID: 32798136 DOI: 10.1016/j.sleep.2020.04.010] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
60 [DOI: 10.1145/3313831.3376290] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
61 Brink-Kjaer A, Olesen AN, Peppard PE, Stone KL, Jennum P, Mignot E, Sorensen HBD. Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness. Clin Neurophysiol 2020;131:1187-203. [PMID: 32299002 DOI: 10.1016/j.clinph.2020.02.027] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
62 Lim DC, Mazzotti DR, Sutherland K, Mindel JW, Kim J, Cistulli PA, Magalang UJ, Pack AI, de Chazal P, Penzel T; SAGIC Investigators. Reinventing polysomnography in the age of precision medicine. Sleep Med Rev 2020;52:101313. [PMID: 32289733 DOI: 10.1016/j.smrv.2020.101313] [Cited by in Crossref: 24] [Cited by in F6Publishing: 21] [Article Influence: 12.0] [Reference Citation Analysis]
63 Berthomier C, Muto V, Schmidt C, Vandewalle G, Jaspar M, Devillers J, Gaggioni G, Chellappa SL, Meyer C, Phillips C, Salmon E, Berthomier P, Prado J, Benoit O, Bouet R, Brandewinder M, Mattout J, Maquet P. Exploring scoring methods for research studies: Accuracy and variability of visual and automated sleep scoring. J Sleep Res 2020;29. [DOI: 10.1111/jsr.12994] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
64 Gunnarsdottir KM, Gamaldo C, Salas RM, Ewen JB, Allen RP, Hu K, Sarma SV. A novel sleep stage scoring system: Combining expert-based features with the generalized linear model. J Sleep Res 2020;29:e12991. [PMID: 32030843 DOI: 10.1111/jsr.12991] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
65 Andrillon T, Solelhac G, Bouchequet P, Romano F, Le Brun M, Brigham M, Chennaoui M, Léger D. Revisiting the value of polysomnographic data in insomnia: more than meets the eye. Sleep Medicine 2020;66:184-200. [DOI: 10.1016/j.sleep.2019.12.002] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 5.5] [Reference Citation Analysis]
66 Kornum BR, Jennum P. The case for narcolepsy as an autoimmune disease. Expert Review of Clinical Immunology 2020;16:231-3. [DOI: 10.1080/1744666x.2020.1719832] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
67 Skorucak J, Hertig-Godeschalk A, Achermann P, Mathis J, Schreier DR. Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment. Front Neurosci 2020;14:8. [PMID: 32038155 DOI: 10.3389/fnins.2020.00008] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
68 Azimi H, Xi P, Bouchard M, Goubran R, Knoefel F. Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat. IEEE Access 2020;8:173428-39. [DOI: 10.1109/access.2020.3025808] [Cited by in Crossref: 10] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
69 Wu N, Li Y, Wang S, Duan Y, Xu L, Gao H. A pilot study of the ‘PSGCloud’ — A cloud-based care service delivery and sleep disorders diagnosis system. Part I: Sleep structure and arousal analysis. Clinical eHealth 2020;3:23-30. [DOI: 10.1016/j.ceh.2020.04.001] [Reference Citation Analysis]
70 Antelmi E, Pizza F, Franceschini C, Ferri R, Plazzi G. REM sleep behavior disorder in narcolepsy: A secondary form or an intrinsic feature? Sleep Med Rev 2020;50:101254. [PMID: 31931470 DOI: 10.1016/j.smrv.2019.101254] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.7] [Reference Citation Analysis]
71 Fiorillo L, Puiatti A, Papandrea M, Ratti P, Favaro P, Roth C, Bargiotas P, Bassetti CL, Faraci FD. Automated sleep scoring: A review of the latest approaches. Sleep Medicine Reviews 2019;48:101204. [DOI: 10.1016/j.smrv.2019.07.007] [Cited by in Crossref: 44] [Cited by in F6Publishing: 28] [Article Influence: 14.7] [Reference Citation Analysis]
72 Papini GB, Fonseca P, van Gilst MM, van Dijk JP, Pevernagie DAA, Bergmans JWM, Vullings R, Overeem S. Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features. Sci Rep 2019;9:17448. [PMID: 31772228 DOI: 10.1038/s41598-019-53403-y] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
73 Pépin J, Bailly S, Tamisier R. Big Data in sleep apnoea: Opportunities and challenges. Respirology 2020;25:486-94. [DOI: 10.1111/resp.13669] [Cited by in Crossref: 14] [Cited by in F6Publishing: 8] [Article Influence: 4.7] [Reference Citation Analysis]
74 Vandi S, Rodolfi S, Pizza F, Moresco M, Antelmi E, Ferri R, Mignot E, Plazzi G, Silvani A. Cardiovascular autonomic dysfunction, altered sleep architecture, and muscle overactivity during nocturnal sleep in pediatric patients with narcolepsy type 1. Sleep 2019;42:zsz169. [DOI: 10.1093/sleep/zsz169] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 2.7] [Reference Citation Analysis]
75 Bassetti CLA, Adamantidis A, Burdakov D, Han F, Gay S, Kallweit U, Khatami R, Koning F, Kornum BR, Lammers GJ, Liblau RS, Luppi PH, Mayer G, Pollmächer T, Sakurai T, Sallusto F, Scammell TE, Tafti M, Dauvilliers Y. Narcolepsy — clinical spectrum, aetiopathophysiology, diagnosis and treatment. Nat Rev Neurol 2019;15:519-39. [DOI: 10.1038/s41582-019-0226-9] [Cited by in Crossref: 113] [Cited by in F6Publishing: 89] [Article Influence: 37.7] [Reference Citation Analysis]
76 Pizza F, Filardi M, Moresco M, Antelmi E, Vandi S, Neccia G, Mazzoni A, Plazzi G. Excessive daytime sleepiness in narcolepsy and central nervous system hypersomnias. Sleep Breath 2020;24:605-14. [DOI: 10.1007/s11325-019-01867-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
77 Chambon S, Thorey V, Arnal PJ, Mignot E, Gramfort A. DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal. J Neurosci Methods 2019;321:64-78. [PMID: 30946878 DOI: 10.1016/j.jneumeth.2019.03.017] [Cited by in Crossref: 22] [Cited by in F6Publishing: 11] [Article Influence: 7.3] [Reference Citation Analysis]
78 Phan H, Andreotti F, Cooray N, Chen OY, De Vos M. SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging. IEEE Trans Neural Syst Rehabil Eng 2019;27:400-10. [PMID: 30716040 DOI: 10.1109/TNSRE.2019.2896659] [Cited by in Crossref: 62] [Cited by in F6Publishing: 14] [Article Influence: 20.7] [Reference Citation Analysis]