BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

228 related articles for article (PubMed ID: 24909981)

  • 1. Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research.
    O'Reilly C; Gosselin N; Carrier J; Nielsen T
    J Sleep Res; 2014 Dec; 23(6):628-635. PubMed ID: 24909981
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24 x 7 utilizing the Siesta database.
    Anderer P; Gruber G; Parapatics S; Woertz M; Miazhynskaia T; Klosch G; Saletu B; Zeitlhofer J; Barbanoj MJ; Danker-Hopfe H; Himanen SL; Kemp B; Penzel T; Grozinger M; Kunz D; Rappelsberger P; Schlogl A; Dorffner G
    Neuropsychobiology; 2005; 51(3):115-33. PubMed ID: 15838184
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters.
    Stepnowsky C; Levendowski D; Popovic D; Ayappa I; Rapoport DM
    Sleep Med; 2013 Nov; 14(11):1199-207. PubMed ID: 24047533
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging.
    Charbonnier S; Zoubek L; Lesecq S; Chapotot F
    Comput Biol Med; 2011 Jun; 41(6):380-9. PubMed ID: 21497802
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multivariate analysis of full-term neonatal polysomnographic data.
    Gerla V; Paul K; Lhotska L; Krajca V
    IEEE Trans Inf Technol Biomed; 2009 Jan; 13(1):104-10. PubMed ID: 19129029
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Relationship between sleep stages and nocturnal trapezius muscle activity.
    Müller C; Nicoletti C; Omlin S; Brink M; Läubli T
    J Electromyogr Kinesiol; 2015 Jun; 25(3):457-62. PubMed ID: 25765124
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.
    Lajnef T; Chaibi S; Ruby P; Aguera PE; Eichenlaub JB; Samet M; Kachouri A; Jerbi K
    J Neurosci Methods; 2015 Jul; 250():94-105. PubMed ID: 25629798
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automatic sleep stage classification using two-channel electro-oculography.
    Virkkala J; Hasan J; Värri A; Himanen SL; Müller K
    J Neurosci Methods; 2007 Oct; 166(1):109-15. PubMed ID: 17681382
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A rule-based automatic sleep staging method.
    Liang SF; Kuo CE; Hu YH; Cheng YS
    Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():6067-70. PubMed ID: 22255723
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.
    Jiang D; Ma Y; Wang Y
    Comput Methods Programs Biomed; 2019 Sep; 178():19-30. PubMed ID: 31416548
    [TBL] [Abstract][Full Text] [Related]  

  • 11. The use of two-channel electro-oculography in automatic detection of unintentional sleep onset.
    Virkkala J; Hasan J; Värri A; Himanen SL; Härmä M
    J Neurosci Methods; 2007 Jun; 163(1):137-44. PubMed ID: 17376536
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Inter-rater reliability of sleep cyclic alternating pattern (CAP) scoring and validation of a new computer-assisted CAP scoring method.
    Ferri R; Bruni O; Miano S; Smerieri A; Spruyt K; Terzano MG
    Clin Neurophysiol; 2005 Mar; 116(3):696-707. PubMed ID: 15721084
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A rule-based automatic sleep staging method.
    Liang SF; Kuo CE; Hu YH; Cheng YS
    J Neurosci Methods; 2012 Mar; 205(1):169-76. PubMed ID: 22245090
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Automatic detection of slow wave sleep using two channel electro-oculography.
    Virkkala J; Hasan J; Värri A; Himanen SL; Müller K
    J Neurosci Methods; 2007 Feb; 160(1):171-7. PubMed ID: 16965823
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Visual and computer-based detection of slow eye movements in overnight and 24-h EOG recordings.
    Magosso E; Ursino M; Zaniboni A; Provini F; Montagna P
    Clin Neurophysiol; 2007 May; 118(5):1122-33. PubMed ID: 17368090
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Open-source logic-based automated sleep scoring software using electrophysiological recordings in rats.
    Gross BA; Walsh CM; Turakhia AA; Booth V; Mashour GA; Poe GR
    J Neurosci Methods; 2009 Oct; 184(1):10-8. PubMed ID: 19615408
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Visual and automatic classification of the cyclic alternating pattern in electroencephalography during sleep.
    Largo R; Lopes MC; Spruyt K; Guilleminault C; Wang YP; Rosa AC
    Braz J Med Biol Res; 2019 Feb; 52(3):e8059. PubMed ID: 30810623
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automatic sleep scoring: a search for an optimal combination of measures.
    Krakovská A; Mezeiová K
    Artif Intell Med; 2011 Sep; 53(1):25-33. PubMed ID: 21742473
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Home monitoring of sleep with a temporary-tattoo EEG, EOG and EMG electrode array: a feasibility study.
    Shustak S; Inzelberg L; Steinberg S; Rand D; David Pur M; Hillel I; Katzav S; Fahoum F; De Vos M; Mirelman A; Hanein Y
    J Neural Eng; 2019 Apr; 16(2):026024. PubMed ID: 30566912
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Assessment of the suitability of using a forehead EEG electrode set and chin EMG electrodes for sleep staging in polysomnography.
    Myllymaa S; Muraja-Murro A; Westeren-Punnonen S; Hukkanen T; Lappalainen R; Mervaala E; Töyräs J; Sipilä K; Myllymaa K
    J Sleep Res; 2016 Dec; 25(6):636-645. PubMed ID: 27230805
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.