AI capable of detecting restless sleep is nothing new — in April, researchers at Stanford and Université Paris-Saclay proposed a system that can predict the location, duration, and type of sleep events in , and in November, Oxford scientists described a framework that could automatically detect . But a method described in a preprint paper published on (““) takes a slightly different tack than most: rather than look for patterns of disordered sleep in slices of sensor data, it takes into account a range collected during polysomnography (sleep studies). This, the paper’s authors say, is what helped nab it first place in Computing in Cardiology’s 2018 PhysioNet challenge for detecting sleep arousal.

“Very little research has been done concerning the effect that non-apnea [and] hypopnea

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