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Extraction of breathing features using MS Kinect for sleep stage detection

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Abstract

This paper presents the contactless measuring of breathing using the MS Kinect depth sensor and compares the results obtained with records of breathing taken by polysomnography (PSG). We explore the methods of signal denoising, resampling, and spectral analysis of acquired data as well as feature extraction and their Bayesian classification. The proposed methodology was applied for analysis of the long-term monitoring of individuals who were observed simultaneously by PSG and MS Kinect in the sleep laboratory. After time synchronization of polysomnographic and MS Kinect video data, features were extracted from both signals and compared. The average error of the frequency while being evaluated by MS Kinect that was related to that obtained by PSG was 3.75 %. The mean accuracy of the Bayesian classification of features into two classes (i.e. wake or sleep) was 88.90 and 88.95 % for the PSG and MS Kinect measurements, respectively. The strong likeness of features supports the hypothesis that contactless techniques may represent a valid alternative to the present approach of sleep monitoring, thereby allowing data acquisition in the home environment as well.

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Acknowledgments

All measured data have been recorded in the University Hospital Hradec Králové.

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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

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

    Aleš Procházka & Martin Schätz

  3. Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy

    Fabio Centonze

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

    Jiří Kuchyňka, Oldřich Vyšata & Martin Vališ

Authors
  1. Aleš Procházka
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  2. Martin Schätz
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  3. Fabio Centonze
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  4. Jiří Kuchyňka
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  5. Oldřich Vyšata
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Correspondence to Aleš Procházka.

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Procházka, A., Schätz, M., Centonze, F. et al. Extraction of breathing features using MS Kinect for sleep stage detection. SIViP 10, 1279–1286 (2016). https://doi.org/10.1007/s11760-016-0897-2

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  • DOI: https://doi.org/10.1007/s11760-016-0897-2

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