  In this paper, we propose a Siamese sleep transformer (SST) that effectively
extracts features from single-channel raw electroencephalogram signals for
robust sleep stage scoring. Despite the significant advances in sleep stage
scoring in the last few years, most of them mainly focused on the increment of
model performance. However, other problems still exist: the bias of labels in
datasets and the instability of model performance by repetitive training. To
alleviate these problems, we propose the SST, a novel sleep stage scoring model
with a selective batch sampling strategy and self-knowledge distillation. To
evaluate how robust the model was to the bias of labels, we used different
datasets for training and testing: the sleep heart health study and the
Sleep-EDF datasets. In this condition, the SST showed competitive performance
in sleep stage scoring. In addition, we demonstrated the effectiveness of the
selective batch sampling strategy with a reduction of the standard deviation of
performance by repetitive training. These results could show that SST extracted
effective learning features against the bias of labels in datasets, and the
selective batch sampling strategy worked for the model robustness in training.
