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Sleep scoring using polysomnography data features

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28, Prague, Czech Republic

    Aleš Procházka, Martin Schätz & Mohammadreza Yadollahi

  2. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36, Prague, Czech Republic

    Aleš Procházka

  3. Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University in Prague, 500 05, Prague, Czech Republic

    Jiří Kuchyňka, Oldřich Vyšata & Martin Vališ

  4. School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Saeid Sanei

Authors
  1. Aleš Procházka
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  2. Jiří Kuchyňka
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  3. Oldřich Vyšata
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  4. Martin Schätz
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  5. Mohammadreza Yadollahi
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  6. Saeid Sanei
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  7. Martin Vališ
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Correspondence to Aleš Procházka.

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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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Procházka, A., Kuchyňka, J., Vyšata, O. et al. Sleep scoring using polysomnography data features. SIViP 12, 1043–1051 (2018). https://doi.org/10.1007/s11760-018-1252-6

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  • DOI: https://doi.org/10.1007/s11760-018-1252-6

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