Rather than look for patterns of disordered sleep in slices of sensor data, a preprint paper takes into account a range of data collected during polysomnography, reports VentureBeat.

“Very little research has been done concerning the effect that non-apnea [and] hypopnea arousals have on sleep quality and general health because they are difficult to detect, [and] sleep arousals have been shown to have lower inter-scorer reliability when compared to apnea [and] hypopnea,” the paper’s authors write. “A more robust method of detecting [sleep] arousals would allow health researchers to determine the effects that these events have on health, as well as develop more effective treatments to reduce their frequency. The purpose of this work is to determine how accurately … arousals can be detected with the use of deep learning methods.”