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Signal, Image and Video Processing

, Volume 12, Issue 6, pp 1043–1051 | Cite as

Sleep scoring using polysomnography data features

  • Aleš Procházka
  • Jiří Kuchyňka
  • Oldřich Vyšata
  • Martin Schätz
  • Mohammadreza Yadollahi
  • Saeid Sanei
  • Martin Vališ
Original Paper
  • 175 Downloads

Abstract

The paper is devoted to the analysis of multichannel biomedical signals acquired in the sleep laboratory. The data analyzed represent polysomnographic records of (i) 33 healthy individuals, (ii) 25 individuals with sleep apnea, and (iii) 18 individuals with sleep apnea and restless leg syndrome. The initial statistical analysis of the sleep segments points to an increase in the number of Wake stages and the decrease in REM stages with increase in age. The goal of the study is visualization of features associated with sleep stages as specified by an experienced neurologist and in their adaptive classification. The results of the support vector machine classifier are compared with those obtained by the k-nearest neighbors method, decision tree and neural network classification using sigmoidal and Bayesian transfer functions. The achieved accuracy for the classification into two classes (to separate the Wake stage from one of NonREM and REM stages) is between 85.6 and 97.5% for the given set of patients with sleep apnea. The proposed models allow adaptive modification of the model coefficients during the learning process to increase the diagnostic efficiency of sleep disorder analysis, in both the clinical and home environments.

Keywords

Polysomnography Feature extraction Sleep stages Classification Computational intelligence Scientific visualization Neural networks 

Notes

Acknowledgements

All data were kindly provided by the Sleep Laboratory of the Faculty Hospital in Hradec Králové, Czech Republic.

Compliance with ethical standards

Ethical Approval

All procedures involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computing and Control EngineeringUniversity of Chemistry and Technology in PraguePragueCzech Republic
  2. 2.Czech Institute of Informatics, Robotics and CyberneticsCzech Technical University in PraguePragueCzech Republic
  3. 3.Department of Neurology, Faculty of Medicine in Hradec KrálovéCharles University in PraguePragueCzech Republic
  4. 4.School of Science and TechnologyNottingham Trent UniversityNottinghamUK

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