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2016 | OriginalPaper | Buchkapitel

Sleep Pattern Discovery via Visualizing Cluster Dynamics of Sound Data

verfasst von : Hongle Wu, Takafumi Kato, Tomomi Yamada, Masayuki Numao, Ken-ichi Fukui

Erschienen in: Trends in Applied Knowledge-Based Systems and Data Science

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Sleep Pattern Discovery via Visualizing Cluster Dynamics of Sound Data
verfasst von
Hongle Wu
Takafumi Kato
Tomomi Yamada
Masayuki Numao
Ken-ichi Fukui
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-42007-3_40