Sleep has a critical role in promoting health. The polysomnography (PSG) and the analysis of the sleep recordings are the main tools to evaluate sleep and to identify sleep disorders. Sleep scoring is an essential procedure for sleep analysis. Its automation is gaining importance over years. It reduces the time-consuming tedious repetitive work of the physicians and it improves the reproducibility of the results. The aim of this work is to optimize the performance of an existing deep learning-based automated sleep scoring system. The innovative idea is that, to simplify and to enhance the automatic scoring procedure, the learning architecture need to be trained on sub-groups of PSG recordings.