  AASM guidelines are the result of decades of efforts aiming at standardizing
sleep scoring procedure, with the final goal of sharing a worldwide common
methodology. The guidelines cover several aspects from the technical/digital
specifications,e.g., recommended EEG derivations, to detailed sleep scoring
rules accordingly to age. Automated sleep scoring systems have always largely
exploited the standards as fundamental guidelines. In this context, deep
learning has demonstrated better performance compared to classical machine
learning. Our present work shows that a deep learning based sleep scoring
algorithm may not need to fully exploit the clinical knowledge or to strictly
adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a
state-of-the-art sleep scoring algorithm, can be strong enough to solve the
scoring task even using clinically non-recommended or non-conventional
derivations, and with no need to exploit information about the chronological
age of the subjects. We finally strengthen a well-known finding that using data
from multiple data centers always results in a better performing model compared
with training on a single cohort. Indeed, we show that this latter statement is
still valid even by increasing the size and the heterogeneity of the single
data cohort. In all our experiments we used 28528 polysomnography studies from
13 different clinical studies.
