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1D Convolutional Neural Network Models for Sleep Arousal Detection
[article]
2019
arXiv
pre-print
Sleep arousals transition the depth of sleep to a more superficial stage. The occurrence of such events is often considered as a protective mechanism to alert the body of harmful stimuli. Thus, accurate sleep arousal detection can lead to an enhanced understanding of the underlying causes and influencing the assessment of sleep quality. Previous studies and guidelines have suggested that sleep arousals are linked mainly to abrupt frequency shifts in EEG signals, but the proposed rules are shown
arXiv:1903.01552v1
fatcat:uaujed3xqrebfkchssqf7vpv3e