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Sleep Pattern Discovery via Visualizing Cluster Dynamics of Sound Data

  • Hongle WuEmail author
  • Takafumi Kato
  • Tomomi Yamada
  • Masayuki Numao
  • Ken-ichi Fukui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9799)

Abstract

The quality of a good sleep is important for a healthy life. Recently, several sleep analysis products have emerged on the market; however, many of them require additional hardware or there is a lack of scientific evidence regarding their clinical efficacy. This paper proposes a novel method for discovering the sleep pattern via clustering of sound events. The sleep-related sound clips are extracted from sound recordings obtained when sleeping. Then, various self-organizing map algorithms are applied to the extracted sound data. We demonstrate the superiority of Kullback-Leibler divergence and obtain the cluster maps to visualize the distribution and changing patterns of sleep-related events during the sleep. Also, we perform a comparative interpretation between sleep stage sequences and obtained cluster maps. The proposed method requires few additional hardware, and its consistency with the medical evidence proves its reliability.

Keywords

Sleep pattern Sound data Self-organizing map Pairwise F-measure Polysomnography 

Notes

Acknowledgments

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hongle Wu
    • 1
    Email author
  • Takafumi Kato
    • 2
  • Tomomi Yamada
    • 2
  • Masayuki Numao
    • 1
  • Ken-ichi Fukui
    • 1
  1. 1.The Institute of Scientific and Industrial ResearchOsaka UniversitySuitaJapan
  2. 2.Graduate School of DentistryOsaka UniversitySuitaJapan

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