  Automatic sleep staging has been often treated as a simple classification
problem that aims at determining the label of individual target polysomnography
(PSG) epochs one at a time. In this work, 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.
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 sequential modelling.
At the sequence processing level, a recurrent layer placed on top of the
learned epoch-wise features for long-term modelling of sequential epochs. The
classification is then carried out on the output vectors at every time step of
the top recurrent layer to produce the sequence of output labels. Despite being
hierarchical, we present a strategy to train the network in an end-to-end
fashion. We show that the proposed network outperforms state-of-the-art
approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of
87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.
