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

RobustSleepNet: Transfer learning for automated sleep staging at scale