RobustSleepNet: Transfer learning for automated sleep staging at scale

TL;DR

RobustSleepNet is a deep learning model designed for automatic sleep staging that can handle various PSG setups and demographic differences, enabling high-quality out-of-the-box performance across diverse clinical datasets.

Contribution

This paper introduces RobustSleepNet, a novel transfer learning approach that achieves robust sleep staging across heterogeneous datasets and PSG montages, addressing limitations of previous methods.

Findings

Achieves 97% of the F1 score of models trained on specific datasets when evaluated on unseen data.

Handles arbitrary PSG montages without retraining, facilitating clinical deployment.

Finetuning improves performance by 2% F1 score on specific populations.

Abstract

Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a preliminary step of this examination, sleep stages are systematically determined. In practice, sleep stage classification relies on the visual inspection of 30-second epochs of polysomnography signals. Numerous automatic approaches have been developed to replace this tedious and expensive task. Although these methods demonstrated better performance than human sleep experts on specific datasets, they remain largely unused in sleep clinics. The main reason is that each sleep clinic uses a specific PSG montage that most automatic approaches cannot handle out-of-the-box. Moreover, even when the PSG montage is compatible, publications have shown that automatic approaches perform poorly on unseen data with different demographics. To address these issues, we introduce RobustSleepNet, a deep learning model for automatic sleep stage classification able to handle arbitrary PSG montages. We trained and evaluated this model in a leave-one-out-dataset fashion on a large corpus of 8 heterogeneous sleep staging datasets to make it robust to demographic changes. When evaluated on an unseen dataset, RobustSleepNet reaches 97% of the F1 of a model explicitly trained on this dataset. Hence, RobustSleepNet unlocks the possibility to perform high-quality out-of-the-box automatic sleep staging with any clinical setup. We further show that finetuning RobustSleepNet, using a part of the unseen dataset, increases the F1 by 2% when compared to a model trained specifically for this dataset. Therefore, finetuning might be used to reach a state-of-the-art level of performance on a specific population.