  Study Objectives: Inter-scorer variability in scoring polysomnograms is a
well-known problem. Most of the existing automated sleep scoring systems are
trained using labels annotated by a single scorer, whose subjective evaluation
is transferred to the model. When annotations from two or more scorers are
available, the scoring models are usually trained on the scorer consensus. The
averaged scorer's subjectivity is transferred into the model, losing
information about the internal variability among different scorers. In this
study, we aim to insert the multiple-knowledge of the different physicians into
the training procedure. The goal is to optimize a model training, exploiting
the full information that can be extracted from the consensus of a group of
scorers.
  Methods: We train two lightweight deep learning based models on three
different multi-scored databases. We exploit the label smoothing technique
together with a soft-consensus (LSSC) distribution to insert the
multiple-knowledge in the training procedure of the model. We introduce the
averaged cosine similarity metric (ACS) to quantify the similarity between the
hypnodensity-graph generated by the models with-LSSC and the hypnodensity-graph
generated by the scorer consensus.
  Results: The performance of the models improves on all the databases when we
train the models with our LSSC. We found an increase in ACS (up to 6.4%)
between the hypnodensity-graph generated by the models trained with-LSSC and
the hypnodensity-graph generated by the consensus.
  Conclusion: Our approach definitely enables a model to better adapt to the
consensus of the group of scorers. Future work will focus on further
investigations on different scoring architectures and hopefully
large-scale-heterogeneous multi-scored datasets.
