  Sleep staging based on electroencephalogram (EEG) plays an important role in
the clinical diagnosis and treatment of sleep disorders. In order to emancipate
human experts from heavy labeling work, deep neural networks have been employed
to formulate automated sleep staging systems recently. However, EEG signals
lose considerable detailed information in network propagation, which affects
the representation of deep features. To address this problem, we propose a new
framework, called MRNet, for data-driven sleep staging by integrating a
multi-scale feature fusion model and a Markov-based sequential correction
algorithm. The backbone of MRNet is a residual block-based network, which
performs as a feature extractor.Then the fusion model constructs a feature
pyramid by concatenating the outputs from the different depths of the backbone,
which can help the network better comprehend the signals in different scales.
The Markov-based sequential correction algorithm is designed to reduce the
output jitters generated by the classifier. The algorithm depends on a prior
stage distribution associated with the sleep stage transition rule and the
Markov chain. Experiment results demonstrate the competitive performance of our
proposed approach on both accuracy and F1 score (e.g., 85.14% Acc and 78.91% F1
score on Sleep-EDFx, and 87.59% Acc and 79.62% F1 score on Sleep-EDF).
