Drowsiness can reduce working efficiency and result in dozing while driving. It is known that short naps are effective for eliminating drowsiness, so further analysis of short naps is required to clarify the relationship between nap's length and quality. EEG in short naps 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 sleep spindles in the time domain using a Long Short-Term Memory (LSTM) network.
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