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

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

31 Oct 2019arXiv:1911.03221archive 2025-07-28

Antoine Guillot, Fabien Sauvet, Emmanuel H. During, Valentin Thorey

Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a single human scorer annotation despite an inter-rater agreement of about 85 % only. The present study introduces two publicly-available datasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patients suffering from obstructive sleep apnea (OSA). Both datasets have been scored by 5 sleep technologists from different sleep centers. We developed a framework to compare automated approaches to a consensus of multiple human scorers. Using this framework, we benchmarked and compared the main literature approaches. We also developed and benchmarked a new deep learning method, SimpleSleepNet, inspired by current state-of-the-art. We demonstrated that many methods can reach human-level performance on both datasets. SimpleSleepNet achieved an F1 of 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 automated sleep staging outperforms human scorers performance for healthy volunteers and patients suffering from OSA. Consideration could be made to use automated approaches in the clinical setting.

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Tasks

Automatic Sleep Stage ClassificationMultimodal Sleep Stage DetectionSleep Stage DetectionSleep Staging

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Sleep Stage Detection DODH SimpleSleepNet Accuracy 89.9 #1 of 2 Archive leaderboard report
Sleep Stage Detection DODH SimpleSleepNet Kappa 84.6 #1 of 2 Archive leaderboard report
Sleep Stage Detection DODH DeepSleepNet Accuracy 89.6 #2 of 2 Archive leaderboard report
Sleep Stage Detection DODH DeepSleepNet Kappa 84.3 #2 of 2 Archive leaderboard report
Sleep Stage Detection DODO SimpleSleepNet Accuracy 88.7 #1 of 3 Archive leaderboard report
Sleep Stage Detection DODO SimpleSleepNet Kappa 82.3 #1 of 3 Archive leaderboard report
Sleep Stage Detection DODO DeepSleepNet Accuracy 87.5 #2 of 3 Archive leaderboard report
Sleep Stage Detection DODO DeepSleepNet Kappa 80.4 #2 of 3 Archive leaderboard report
Sleep Stage Detection DODO SeqSleepNet Accuracy 85.5 #3 of 3 Archive leaderboard report
Sleep Stage Detection DODO SeqSleepNet Kappa 77.2 #3 of 3 Archive leaderboard report
Sleep Stage Detection MASS SS3 Deep Sleep Net Accuracy 89.1% #1 of 6 Archive leaderboard report
Sleep Stage Detection MASS SS3 Simple Sleep Net Accuracy 88.8% #2 of 6 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

GRU

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