  Correctly identifying sleep stages is important in diagnosing and treating
sleep disorders. This work proposes a joint classification-and-prediction
framework based on CNNs for automatic sleep staging, and, subsequently,
introduces a simple yet efficient CNN architecture to power the framework.
Given a single input epoch, the novel framework jointly determines its label
(classification) and its neighboring epochs' labels (prediction) in the
contextual output. While the proposed framework is orthogonal to the widely
adopted classification schemes, which take one or multiple epochs as contextual
inputs and produce a single classification decision on the target epoch, we
demonstrate its advantages in several ways. First, it leverages the dependency
among consecutive sleep epochs while surpassing the problems experienced with
the common classification schemes. Second, even with a single model, the
framework has the capacity to produce multiple decisions, which are essential
in obtaining a good performance as in ensemble-of-models methods, with very
little induced computational overhead. Probabilistic aggregation techniques are
then proposed to leverage the availability of multiple decisions. We conducted
experiments on two public datasets: Sleep-EDF Expanded with 20 subjects, and
Montreal Archive of Sleep Studies dataset with 200 subjects. The proposed
framework yields an overall classification accuracy of 82.3% and 83.6%,
respectively. We also show that the proposed framework not only is superior to
the baselines based on the common classification schemes but also outperforms
existing deep-learning approaches. To our knowledge, this is the first work
going beyond the standard single-output classification to consider multitask
neural networks for automatic sleep staging. This framework provides avenues
for further studies of different neural-network architectures for automatic
sleep staging.
