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.

翻译：ASM指导方针是数十年来使睡眠评分程序标准化的努力的结果,其最终目标是共享全球通用方法。指导方针涵盖技术/数字规格的若干方面,例如,建议EEEG衍生方法,以便根据年龄来详细确定睡眠评分规则。自动睡眠评分系统一直主要利用这些标准作为基本准则。在这方面,深层次的学习表明,与古典机器学习相比,成绩较好。我们目前的工作表明,深层次的基于学习的睡眠评分算法可能不需要充分利用临床知识或严格遵守AASM指导方针。具体地说,我们证明,U-Sleep(一种最先进的睡眠评分算法)可能足够强大,足以解决评分任务,即使使用临床上不推荐或非常规的推算法,也没有必要利用有关学科时间长短的信息。我们最后加强了一个众所周知的发现,即使用多个数据中心的数据与单个群组的培训相比,总是产生更好的业绩模式。事实上,我们证明后一种说法仍然有效,即使我们增加了单类临床实验的大小和13项临床实验所使用的单项数据组。</s>