  Neural networks often obtain sub-optimal representations during training,
which degrade robustness as well as classification performances. This is a
severe problem in applying deep learning to bio-medical domains, since models
are vulnerable to being harmed by irregularities and scarcities in data. In
this study, we propose a pre-training technique that handles this challenge in
sleep staging tasks. Inspired by conventional methods that experienced
physicians have used to classify sleep states from the existence of
characteristic waveform shapes, or template patterns, our method introduces a
cosine similarity based convolutional neural network to extract representative
waveforms from training data. Afterwards, these features guide a model to
construct representations based on template patterns. Through extensive
experiments, we demonstrated that guiding a neural network with template
patterns is an effective approach for sleep staging, since (1) classification
performances are significantly enhanced and (2) robustness in several aspects
are improved. Last but not least, interpretations on models showed that notable
features exploited by trained experts are correctly addressed during prediction
in the proposed method.
