AbstractStudy ObjectivesInter-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.MethodsWe train two lightweight deep learning-based models on three different multi-scored databases. We exploit the label smoothing technique together with asoft-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-LSSCand the hypnodensity-graph generated by the scorer consensus.ResultsThe performance of the models improves on all the databases when we train the models with ourLSSC. We found an increase inACS(up to 6.4%) between the hypnodensity-graph generated by the models trained with-LSSCand the hypnodensity-graph generated by the consensus.ConclusionOur 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.