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

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.

Fig. 1

Experiment room

Fig. 2

An example of an extracted sound wave and frequency spectrum

Fig. 3

Dendrogram by HC

Fig. 4

Silhouette coefficient on different stop-criteria

Fig. 5

Major clusters on KL-KSOM cluster map with frequency spectrum of event examples from each cluster

Fig. 6

Transition probability matrices of HMMs. a HMMs trained on sound events. b HMMs trained on sleep stage sequences