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Sleep stage classification constitutes an important element of sleep disorderdiagnosis. It relies on the visual inspection of polysomnography records bytrained sleep technologists. Automated approaches have been designed toalleviate this resource-intensive task. However, such approaches are usuallycompared to a single human scorer annotation despite an inter-rater agreementof about 85 % only. The present study introduces two publicly-availabledatasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patientssuffering from obstructive sleep apnea (OSA). Both datasets have been scored by5 sleep technologists from different sleep centers. We developed a framework tocompare automated approaches to a consensus of multiple human scorers. Usingthis framework, we benchmarked and compared the main literature approaches. Wealso developed and benchmarked a new deep learning method, SimpleSleepNet,inspired by current state-of-the-art. We demonstrated that many methods canreach human-level performance on both datasets. SimpleSleepNet achieved an F1of 89.9 % vs 86.8 % on average for human scorers on DOD-H, and an F1 of 88.3 %vs 84.8 % on DOD-O. Our study highlights that using state-of-the-art automatedsleep staging outperforms human scorers performance for healthy volunteers andpatients suffering from OSA. Consideration could be made to use automatedapproaches in the clinical setting.

Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging