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International Conference on Diagnostics of Processes and Systems

DPS 2017: Advanced Solutions in Diagnostics and Fault Tolerant Control pp 328–335Cite as

Detection of Apnea–Hypopnea Events Using Actigraphy and Sleep Sounds

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 635))

Abstract

In this work a new method of automatic detection of apnea–hypopnea episodes is presented. It combines snore/nonsnore classification with information about body and limbs movements. The snore/nonsnore detection is performed using Discrete Fourier Transform and energy calculation. The feature space is reduced using Linear Discriminant Analysis and a linear classifier was obtained. The feasibility of this method was tested on the set of 8 full-night polysomnography recordings of which 2 indicate sleep apnea syndrome. The result shows that the method is effective in detection of apneic events.

This work was partially supported by the Warsaw University of Technology, Faculty of Mechatronics Dean’s Grant 504/02801.

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Authors and Affiliations

  1. Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Kornel Rostek

  2. DreamJay Inc., San Francisco, USA

    Kornel Rostek

Authors
  1. Kornel Rostek
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Correspondence to Kornel Rostek .

Editor information

Editors and Affiliations

  1. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Jan M. Kościelny

  2. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Michał Syfert

  3. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Anna Sztyber

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Rostek, K. (2018). Detection of Apnea–Hypopnea Events Using Actigraphy and Sleep Sounds. In: Kościelny, J., Syfert, M., Sztyber, A. (eds) Advanced Solutions in Diagnostics and Fault Tolerant Control. DPS 2017. Advances in Intelligent Systems and Computing, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-319-64474-5_27

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  • DOI: https://doi.org/10.1007/978-3-319-64474-5_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64473-8

  • Online ISBN: 978-3-319-64474-5

  • eBook Packages: EngineeringEngineering (R0)

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