RobustSleepNet: Transfer Learning for Automated Sleep Staging at Scale

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2021, Vol.29, p.1441-1451
• Main Authors: Guillot, Antoine, Thorey, Valentin
• Format: Article
• Language: English
• Subjects: Automated sleep stage classification; Brain modeling; Classification; Datasets; Deep learning; Demographics; Demography; EEG; Electroencephalography; Electromyography; Electrooculography; Human performance; Inspection; Machine learning; PSG; Sleep; Sleep disorders; Training; Transfer learning; Visual signals
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2021.3098968

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.