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

机译:基于声音事件的睡眠质量评估统计睡眠模式建模

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摘要

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.
机译:良好的睡眠对健康生活至关重要。最近,市场上出现了一些家用睡眠设备,声称它们可以提供个人睡眠监控。但是,其中许多都需要额外的硬件,或者缺乏有关其可靠性的科学证据。在本文中,我们提出了一种通过在卧室记录的声音事件评估睡眠质量的新颖方法。我们以主观睡眠质量作为训练标签,结合核自组织图,层次聚类和隐马尔可夫模型等多种机器学习方法,获得了表明特定质量水平睡眠模式的模型。所提出的方法不同于传统的基于睡眠阶段的方法,提供了睡眠监测的新方面,即声音事件与人的睡眠直接相关。

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