122 related articles for article (PubMed ID: 35675746)
1. Certainty about uncertainty in sleep staging: a theoretical framework.
van Gorp H; Huijben IAM; Fonseca P; van Sloun RJG; Overeem S; van Gilst MM
Sleep; 2022 Aug; 45(8):. PubMed ID: 35675746
[TBL] [Abstract][Full Text] [Related]
2. Modeling the Impact of Inter-Rater Disagreement on Sleep Statistics Using Deep Generative Learning.
van Gorp H; van Gilst MM; Fonseca P; Overeem S; van Sloun RJG
IEEE J Biomed Health Inform; 2023 Nov; 27(11):5599-5609. PubMed ID: 37561616
[TBL] [Abstract][Full Text] [Related]
3. Multicentre sleep-stage scoring agreement in the Sleep Revolution project.
Nikkonen S; Somaskandhan P; Korkalainen H; Kainulainen S; Terrill PI; Gretarsdottir H; Sigurdardottir S; Olafsdottir KA; Islind AS; Óskarsdóttir M; Arnardóttir ES; Leppänen T
J Sleep Res; 2024 Feb; 33(1):e13956. PubMed ID: 37309714
[TBL] [Abstract][Full Text] [Related]
4. Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring.
Bakker JP; Ross M; Cerny A; Vasko R; Shaw E; Kuna S; Magalang UJ; Punjabi NM; Anderer P
Sleep; 2023 Feb; 46(2):. PubMed ID: 35780449
[TBL] [Abstract][Full Text] [Related]
5. Dreem Open Datasets: Multi-Scored Sleep Datasets to Compare Human and Automated Sleep Staging.
Guillot A; Sauvet F; During EH; Thorey V
IEEE Trans Neural Syst Rehabil Eng; 2020 Sep; 28(9):1955-1965. PubMed ID: 32746326
[TBL] [Abstract][Full Text] [Related]
6. Studying sleep: towards the identification of hypnogram features that drive expert interpretation.
van der Woerd C; van Gorp H; Dujardin S; Sastry M; Garcia Caballero H; van Meulen F; van den Elzen S; Overeem S; Fonseca P
Sleep; 2024 Mar; 47(3):. PubMed ID: 38038673
[TBL] [Abstract][Full Text] [Related]
7. U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging.
Heremans ERM; Seedat N; Buyse B; Testelmans D; van der Schaar M; De Vos M
Comput Biol Med; 2024 Mar; 171():108205. PubMed ID: 38401452
[TBL] [Abstract][Full Text] [Related]
8. Investigation of an automatic sleep stage classification by means of multiscorer hypnogram.
Figueroa Helland VC; Gapelyuk A; Suhrbier A; Riedl M; Penzel T; Kurths J; Wessel N
Methods Inf Med; 2010; 49(5):467-72. PubMed ID: 20644896
[TBL] [Abstract][Full Text] [Related]
9. The American Academy of Sleep Medicine inter-scorer reliability program: sleep stage scoring.
Rosenberg RS; Van Hout S
J Clin Sleep Med; 2013 Jan; 9(1):81-7. PubMed ID: 23319910
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of Automated Hypnogram Analysis on Multi-Scored Polysomnographies.
Van der Plas D; Verbraecken J; Willemen M; Meert W; Davis J
Front Digit Health; 2021; 3():707589. PubMed ID: 34713177
[TBL] [Abstract][Full Text] [Related]
11. Intrinsic randomness in epidemic modelling beyond statistical uncertainty.
Penn MJ; Laydon DJ; Penn J; Whittaker C; Morgenstern C; Ratmann O; Mishra S; Pakkanen MS; Donnelly CA; Bhatt S
Commun Phys; 2023; 6(1):146. PubMed ID: 38665405
[TBL] [Abstract][Full Text] [Related]
12. An end-to-end framework for real-time automatic sleep stage classification.
Patanaik A; Ong JL; Gooley JJ; Ancoli-Israel S; Chee MWL
Sleep; 2018 May; 41(5):. PubMed ID: 29590492
[TBL] [Abstract][Full Text] [Related]
13. Predicting lymph node metastasis in patients with oropharyngeal cancer by using a convolutional neural network with associated epistemic and aleatoric uncertainty.
Dohopolski M; Chen L; Sher D; Wang J
Phys Med Biol; 2020 Nov; 65(22):225002. PubMed ID: 33179605
[TBL] [Abstract][Full Text] [Related]
14. Inter-expert and intra-expert reliability in sleep spindle scoring.
Wendt SL; Welinder P; Sorensen HB; Peppard PE; Jennum P; Perona P; Mignot E; Warby SC
Clin Neurophysiol; 2015 Aug; 126(8):1548-56. PubMed ID: 25434753
[TBL] [Abstract][Full Text] [Related]
15. Assessment of automated scoring of polysomnographic recordings in a population with suspected sleep-disordered breathing.
Pittman SD; MacDonald MM; Fogel RB; Malhotra A; Todros K; Levy B; Geva AB; White DP
Sleep; 2004 Nov; 27(7):1394-403. PubMed ID: 15586793
[TBL] [Abstract][Full Text] [Related]
16. Progression of Geographic Atrophy: Epistemic Uncertainties Affecting Mathematical Models and Machine Learning.
Arslan J; Benke KK
Transl Vis Sci Technol; 2021 Nov; 10(13):3. PubMed ID: 34727162
[TBL] [Abstract][Full Text] [Related]
17. Inter-rater reliability for identification of REM sleep in Parkinson's disease.
Bliwise DL; Willians ML; Irbe D; Ansari FP; Rye DB
Sleep; 2000 Aug; 23(5):671-6. PubMed ID: 10947035
[TBL] [Abstract][Full Text] [Related]
18. Minimizing Interrater Variability in Staging Sleep by Use of Computer-Derived Features.
Younes M; Hanly PJ
J Clin Sleep Med; 2016 Oct; 12(10):1347-1356. PubMed ID: 27448418
[TBL] [Abstract][Full Text] [Related]
19. Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients.
Schaltenbrand N; Lengelle R; Toussaint M; Luthringer R; Carelli G; Jacqmin A; Lainey E; Muzet A; Macher JP
Sleep; 1996 Jan; 19(1):26-35. PubMed ID: 8650459
[TBL] [Abstract][Full Text] [Related]
20. Process and outcome for international reliability in sleep scoring.
Zhang X; Dong X; Kantelhardt JW; Li J; Zhao L; Garcia C; Glos M; Penzel T; Han F
Sleep Breath; 2015 Mar; 19(1):191-5. PubMed ID: 24801137
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
