SleepAge: Sleep Quality Assessment from Nocturnal Sounds in Home Environment

Abstract

Abstract Although numerous systems for sleep quality assessment are available on the market nowadays, they usually fail due to two main reasons. First, their sleep quality assessment is not based on scientific evidence. Second, many of these systems require additional hardware to identify the sleep state, thus becoming expensive and impractical. We address the first problem by proposing a novel concept, called SleepAge, which considers the relationship between age and sleep quality and then estimates the sleep pattern to evaluate the sleep quality. This concept relies on age because it is generally available from health records and is an objective parameter that can be easily updated. As sounds such as snoring, bruxism, and body movements can reflect sleep patterns, we address the second problem by recording these sounds using a smartphone. We also apply a novel method for data collection that considers participants from a wide range of ages over long recording periods in home environments. Therefore, we can model normal sleep patterns for different age groups. Moreover, we estimate SleepAge from the large repository of sound dataset using various deep learning models. To this end, sound data are represented as spectrograms, and state-of-the-art deep learning models are used for SleepAge estimation. Experimental results indicate accurate estimation, and a network pretrained on different images further enhances the estimation accuracy of unseen data.