Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Oct 30;5(1):11.
doi: 10.1007/s13755-017-0031-z. eCollection 2017 Dec.

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

Hongle Wu  1 , Takafumi Kato  2 , Masayuki Numao  1 , Ken-Ichi Fukui  1
Affiliations

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

Hongle Wu et al. Health Inf Sci Syst. .

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.

Keywords: Hidden Markov model; Hierarchical clustering; Self-organizing map; Sleep quality; Sound data.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Experiment room
Fig. 2
Fig. 2
An example of an extracted sound wave and frequency spectrum
Fig. 3
Fig. 3
Dendrogram by HC
Fig. 4
Fig. 4
Silhouette coefficient on different stop-criteria
Fig. 5
Fig. 5
Major clusters on KL-KSOM cluster map with frequency spectrum of event examples from each cluster
Fig. 6
Fig. 6
Transition probability matrices of HMMs. a HMMs trained on sound events. b HMMs trained on sleep stage sequences
See this image and copyright information in PMC

References

    1. Åkerstedt T, Billiard M, Bonnet M, Ficca G, Garma L, Mariotti M, Salzarulo P, Schulz H. Awakening from sleep. Sleep Med Rev. 2002;6(4):267–286. doi: 10.1053/smrv.2001.0202. - DOI - PubMed
    1. Behar J, Roebuck A, Domingos JS, Gederi E, Clifford GD. A review of current sleep screening applications for smartphones. Physiol. Meas. 2013;34(7):R29–R46. doi: 10.1088/0967-3334/34/7/R29. - DOI - PubMed
    1. Berkhin P. A survey of clustering data mining techniques. In: Grouping multidimensional data. Berlin: Springer; 2006. p. 25–71.
    1. Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus C, Vaughn B. The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications. Darien, IL: American Academy of Sleep Medicine; 2012.
    1. Bonnet MH, Johnson LC. Relationship of arousal threshold to sleep stage distribution and subjective estimates of depth and quality of sleep. Sleep. 1978;1(2):161–168. doi: 10.1093/sleep/1.2.161. - DOI - PubMed

LinkOut - more resources