SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http://github.com/pquochuy/SeqSleepNet). At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term…

• W
  Wellcome

  Award: 098461/Z/12/Z
• WT
  Wellcome Trust

  Awards: 098461, 098461/Z/12/Z
• NI
  National Institute for Health and Care Research
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/N024966/1, EP/N024966/1
• NO
  NIHR Oxford Biomedical Research Centre