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A Study on Automatic Detection of Sleep Spindles using a Long Short-Term Memory Network

Drowsiness can reduce working efficiency and result in dozing while driving. It is known that short naps in sleep stage 2 is effective for eliminating drowsiness, so further analysis of sleep stage 2 are required to clarify the relationship between nap length and quality. EEG in sleep stage 2 is characterized by the appearance of a sleep spindle and detection of this sleep spindle is thus required for analysis of this sleep stage. In this paper, we propose a method to detect a sleep spindle in the time domain using a Long Short-Term Memory (LSTM) network.