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Estimation of Sleep Quality by Using Microstructure Profiles

  • Zuzana Rošt’ákováEmail author
  • Georg Dorffner
  • Önder Aydemir
  • Roman Rosipal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

Abstract

Polysomnograhy is the standard method for objectively measuring sleep, both in patient diagnostics in the sleep laboratory and in clinical research. However, the correspondence between this objective measurement and a person’s subjective assessment of the sleep quality is surprisingly small, if existent. Considering standard sleep characteristics based on the Rechtschaffen and Kales sleep models and the Self-rating Sleep and Awakening Quality scale (SSA), the observed correlations are at most 0.35. An alternative way of sleep modelling - the probabilistic sleep model (PSM) characterises sleep with probability values of standard sleep stages Wake, S1, S2, slow wave sleep (SWS) and REM operating on three second long time segments. We designed sleep features based on the PSM which correspond to the standard sleep characteristics or reflect the dynamical behaviour of probabilistic sleep curves. The main goal of this work is to show whether the continuous sleep representation includes more information about the subjectively experienced quality of sleep than the traditional hypnogram. Using a linear combination of sleep features an improvement in correlation with the subjective sleep quality scores was observed in comparison to the case when a single sleep feature was considered.

Keywords

Probabilistic sleep model Hypnogram Self–rating Sleep and Awakening Quality scale Sleep features 

Notes

Acknowledgements

This research was supported by the Ernst Mach Stipendien der Aktion Österreich–Slowakei ICM-2016-03516, the Slovak Research and Development Agency (grant number APVV–0668–12), the Ministry of Health of the Slovak Republic (grant number MZ 2012/56–SAV–6) and by the VEGA 2/0011/16 grant. Dr. Aydemir’s contribution was supported by a scholarship from The Scientific and Technological Research Council of Turkey (TUBITAK).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zuzana Rošt’áková
    • 1
    • 2
    Email author
  • Georg Dorffner
    • 2
  • Önder Aydemir
    • 2
    • 3
  • Roman Rosipal
    • 1
  1. 1.Institute of Measurement ScienceSlovak Academy of SciencesBratislavaSlovakia
  2. 2.Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics and Intelligent SystemsMedical University of ViennaViennaAustria
  3. 3.Department of Electrical and Electronics EngineeringKaradeniz Technical UniversityTrabzonTurkey

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