U-Sleep's resilience to AASM guidelines
This work challenges the necessity of clinical knowledge in automated sleep scoring, potentially simplifying and broadening the application of such systems.
The study demonstrated that U-Sleep, a deep learning-based sleep scoring algorithm, can effectively perform sleep scoring without strictly adhering to AASM guidelines, such as using non-recommended EEG derivations and ignoring chronological age, while confirming that training on multiple data centers improves performance over single cohorts, using 28,528 polysomnography studies from 13 clinical studies.
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.