Researchers at Osaka University in Japan have developed a tool that uses machine learning to model personal sleep patterns using sounds made during sleep.
The researchers used smartphones to record the sounds of sleeping volunteers, and used modified self-organizing map (SOM) algorithms to analyze the sounds and compare them with polysomnography (PSG) data taken from the same volunteers. PSG measures an assortment of activities during sleep, including brain activity, eye movements, and heart rhythms.
Osaka professor Ken-ichi Fukui says the researchers used the SOM algorithms to visualize the dynamics of sleep. The algorithms extracted very obvious sleeping patterns based on noises such as snoring and teeth grinding.
The researchers say the association of sleep sounds with sleep patterns provides a whole new prospect of sleep diagnostics. They say new apps could be used to record sleep sounds and provide recommendations to improve sleep patterns.
From Asian Scientist
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