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Journal Abstract Search

272 related items for PubMed ID: 26289580

  • 1. Sleep stage classification with ECG and respiratory effort.
    Fonseca P, Long X, Radha M, Haakma R, Aarts RM, Rolink J.
    Physiol Meas; 2015 Oct; 36(10):2027-40. PubMed ID: 26289580
    [Abstract] [Full Text] [Related]

  • 2. Measuring dissimilarity between respiratory effort signals based on uniform scaling for sleep staging.
    Long X, Yang J, Weysen T, Haakma R, Foussier J, Fonseca P, Aarts RM.
    Physiol Meas; 2014 Dec; 35(12):2529-42. PubMed ID: 25407770
    [Abstract] [Full Text] [Related]

  • 3. Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea.
    Redmond SJ, Heneghan C.
    IEEE Trans Biomed Eng; 2006 Mar; 53(3):485-96. PubMed ID: 16532775
    [Abstract] [Full Text] [Related]

  • 4. Respiration amplitude analysis for REM and NREM sleep classification.
    Long X, Foussier J, Fonseca P, Haakma R, Aarts RM.
    Annu Int Conf IEEE Eng Med Biol Soc; 2013 Mar; 2013():5017-20. PubMed ID: 24110862
    [Abstract] [Full Text] [Related]

  • 5. Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram.
    Li Q, Li Q, Liu C, Shashikumar SP, Nemati S, Clifford GD.
    Physiol Meas; 2018 Dec 21; 39(12):124005. PubMed ID: 30524025
    [Abstract] [Full Text] [Related]

  • 6. An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification.
    Willemen T, Van Deun D, Verhaert V, Vandekerckhove M, Exadaktylos V, Verbraecken J, Van Huffel S, Haex B, Sloten JV.
    IEEE J Biomed Health Inform; 2014 Mar 21; 18(2):661-9. PubMed ID: 24058031
    [Abstract] [Full Text] [Related]

  • 7. Sleep and wake classification with actigraphy and respiratory effort using dynamic warping.
    Long X, Fonseca P, Foussier J, Haakma R, Aarts RM.
    IEEE J Biomed Health Inform; 2014 Jul 21; 18(4):1272-84. PubMed ID: 24108754
    [Abstract] [Full Text] [Related]

  • 8. Estimating actigraphy from motion artifacts in ECG and respiratory effort signals.
    Fonseca P, Aarts RM, Long X, Rolink J, Leonhardt S.
    Physiol Meas; 2016 Jan 21; 37(1):67-82. PubMed ID: 26641863
    [Abstract] [Full Text] [Related]

  • 9. Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals.
    Sharma M, Dhiman HS, Acharya UR.
    Comput Biol Med; 2021 Apr 21; 131():104246. PubMed ID: 33631498
    [Abstract] [Full Text] [Related]

  • 10. Sleep stage classification based on respiratory signal.
    Tataraidze A, Anishchenko L, Korostovtseva L, Kooij BJ, Bochkarev M, Sviryaev Y.
    Annu Int Conf IEEE Eng Med Biol Soc; 2015 Apr 21; 2015():358-61. PubMed ID: 26736273
    [Abstract] [Full Text] [Related]

  • 11. Detection of Nocturnal Slow Wave Sleep Based on Cardiorespiratory Activity in Healthy Adults.
    Long X, Fonseca P, Aarts RM, Haakma R, Rolink J, Leonhardt S.
    IEEE J Biomed Health Inform; 2017 Jan 21; 21(1):123-133. PubMed ID: 26452293
    [Abstract] [Full Text] [Related]

  • 12. A method of REM-NREM sleep distinction using ECG signal for unobtrusive personal monitoring.
    Singh J, Sharma RK, Gupta AK.
    Comput Biol Med; 2016 Nov 01; 78():138-143. PubMed ID: 27741420
    [Abstract] [Full Text] [Related]

  • 13. A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing.
    Xie J, Fonseca P, van Dijk J, Overeem S, Long X.
    Biomed Eng Online; 2024 May 05; 23(1):45. PubMed ID: 38705982
    [Abstract] [Full Text] [Related]

  • 14. Unconstrained Sleep Stage Estimation Based on Respiratory Dynamics and Body Movement.
    Hwang SH, Lee YJ, Jeong DU, Park KS.
    Methods Inf Med; 2016 Dec 07; 55(6):545-555. PubMed ID: 27626633
    [Abstract] [Full Text] [Related]

  • 15. 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 Dec 07; 51(3):115-33. PubMed ID: 15838184
    [Abstract] [Full Text] [Related]

  • 16. A comparison of probabilistic classifiers for sleep stage classification.
    Fonseca P, den Teuling N, Long X, Aarts RM.
    Physiol Meas; 2018 May 15; 39(5):055001. PubMed ID: 29620019
    [Abstract] [Full Text] [Related]

  • 17. Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields.
    Fonseca P, den Teuling N, Long X, Aarts RM.
    IEEE J Biomed Health Inform; 2017 Jul 15; 21(4):956-966. PubMed ID: 27076473
    [Abstract] [Full Text] [Related]

  • 18. Automatic detection of overnight deep sleep based on heart rate variability: a preliminary study.
    Long X, Fonseca P, Haakma R, Foussier J, Aarts RM.
    Annu Int Conf IEEE Eng Med Biol Soc; 2014 Jul 15; 2014():50-3. PubMed ID: 25569894
    [Abstract] [Full Text] [Related]

  • 19. Automatic sleep stages classification using respiratory, heart rate and movement signals.
    Gaiduk M, Penzel T, Ortega JA, Seepold R.
    Physiol Meas; 2018 Dec 24; 39(12):124008. PubMed ID: 30524059
    [Abstract] [Full Text] [Related]

  • 20. Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults.
    Fonseca P, Weysen T, Goelema MS, Møst EIS, Radha M, Lunsingh Scheurleer C, van den Heuvel L, Aarts RM.
    Sleep; 2017 Jul 01; 40(7):. PubMed ID: 28838130
    [Abstract] [Full Text] [Related]

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