U-Sleep's resilience to AASM guidelines

Abstract

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.

References in corpus (4)

• A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
• SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
• Modulating early visual processing by language
• Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

Cited by in corpus (3)

• A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input
• Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
• NeuroSleepNet: An Explainable Multi-Head Attention-Based Framework With Spatial and Multi-Scale Independent Temporal Context Learning for Automatic Sleep Stage Scoring