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Analyzing Sleep Stages in Home Environment Based on Ballistocardiography

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Health Information Science (HIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9085))

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Abstract

Currently, a number of people have various sleep disorders, and sleep stages play an important role in assessment of sleep quality and health status. This paper proposes an effective approach of analyzing the sleep stages based on ballistocardiography (BCG), which can be continuously detected with micro-movement sensitive mattress (MSM) in this work, during non-intrusive sleep in home environment. This paper focuses on extracting features from BCG from the following three aspects: multi-resolution wavelet analysis of the heartbeat intervals based time-domain features, Welch’s power spectrum estimation based frequency-domain features and the detrended fluctuation analysis (DFA) value for long term correlation based features. Moreover, the support vector machine (SVM) with or without the factor of sleep rhythm, and recurrent neural network (RNN) are adopted to build the classifiers, and both the personal model and self-independent model are investigated for different scenarios. Experimental result of 56 subjects [25 women and 31 men, aged from 16 to 71] was evaluated applying the proposed method and compared to the result provided by professional visual scoring by ECG and EEG. The SVM with the factor of sleep rhythm shows better performance with an average accuracy between 73.21%~83.94% in the personal model, and the self-independent model also achieves a satisfactory level with an average accuracy of 73.611~78.78% for male and 73.99%~ 79.46% for female.

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Author information

Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnic University, Xian, China

    Hongbo Ni, Tingzhi Zhao, Xingshe Zhou, Zhu Wang & Lei Chen

  2. Aeromedicine Institute of P.L.A, Beijing, China

    Jun Yang

Authors
  1. Hongbo Ni
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  2. Tingzhi Zhao
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  3. Xingshe Zhou
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  4. Zhu Wang
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  5. Lei Chen
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  6. Jun Yang
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Corresponding author

Correspondence to Tingzhi Zhao .

Editor information

Editors and Affiliations

  1. Victoria University, Melbourne, Australia

    Xiaoxia Yin

  2. Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada

    Kendall Ho

  3. University of Arizona, Tucson, Arizona, USA

    Daniel Zeng

  4. University of Nottingham, Nottingham, United Kingdom

    Uwe Aickelin

  5. Victoria University, Melbourne, Australia

    Rui Zhou

  6. Victoria University, Melbourne, Australia

    Hua Wang

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© 2015 Springer International Publishing Switzerland

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Ni, H., Zhao, T., Zhou, X., Wang, Z., Chen, L., Yang, J. (2015). Analyzing Sleep Stages in Home Environment Based on Ballistocardiography. In: Yin, X., Ho, K., Zeng, D., Aickelin, U., Zhou, R., Wang, H. (eds) Health Information Science. HIS 2015. Lecture Notes in Computer Science(), vol 9085. Springer, Cham. https://doi.org/10.1007/978-3-319-19156-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-19156-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19155-3

  • Online ISBN: 978-3-319-19156-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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