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Statistical sleep pattern modelling for sleep quality assessment based on sound events

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Abstract

A good sleep is important for a healthy life. Recently, several consumer sleep devices have emerged on the market claiming that they can provide personal sleep monitoring; however, many of them require additional hardware or there is a lack of scientific evidence regarding their reliability. In this paper we proposed a novel method to assess the sleep quality through sound events recorded in the bedroom. We used subjective sleep quality as training label, combined several machine learning approaches including kernelized self organizing map, hierarchical clustering and hidden Markov model, obtained the models to indicate the sleep pattern of specific quality level. The proposed method is different from traditional sleep stage based method, provides a new aspect of sleep monitoring that sound events are directly correlated with the sleep of a person.

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Notes

  1. https://www.onosokki.co.jp/English/hp_e/products/keisoku/s_v/la1200.html.

  2. http://proav.roland.com/products/r-4_pro/.

  3. https://en.wikipedia.org/wiki/Zeo,_Inc.

  4. http://www.beddit.com/.

  5. https://www.fitbit.com/.

  6. http://sleep.urbandroid.org/.

  7. http://www.sleepcycle.com/.

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Acknowledgements

This research is partially supported by the Center of Innovation Program from Japan Science and Technology Agency, JST, the Grant-in-Aid for Scientific Research (B)(#25293393) from the JSPS, and Challenge to Intractable Oral Diseases from Osaka University Graduate School of Dentistry.

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Authors and Affiliations

  1. Department of Architecture for Intelligence, The Institute of Scientific and Industrial Research, Osaka University, Suita, Japan

    Hongle Wu, Masayuki Numao & Ken-ichi Fukui

  2. Department of Oral Physiology, Graduate School of Dentistry, Osaka University, Suita, Japan

    Takafumi Kato

Authors
  1. Hongle Wu
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  2. Takafumi Kato
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  3. Masayuki Numao
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  4. Ken-ichi Fukui
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Correspondence to Hongle Wu.

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Wu, H., Kato, T., Numao, M. et al. Statistical sleep pattern modelling for sleep quality assessment based on sound events. Health Inf Sci Syst 5, 11 (2017). https://doi.org/10.1007/s13755-017-0031-z

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