Papers › DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal, Emmanuel Mignot, Alexandre Gramfort
Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30s window of signal. For diagnosis, they also rely on shorter prototypical micro-architecture events which exhibit variable durations and shapes, such as spindles, K-complexes or arousals. Annotating such events is traditionally performed by a trained sleep expert, making the process time consuming, tedious and subject to inter-scorer variability. To automate this procedure, various methods have been developed, yet these are event-specific and rely on the extraction of hand-crafted features. New method: We propose a novel deep learning architecure called Dreem One Shot Event Detector (DOSED). DOSED jointly predicts locations, durations and types of events in EEG time series. The proposed approach, applied here on sleep related micro-architecture events, is inspired by object detectors developed for computer vision such as YOLO and SSD. It relies on a convolutional neural network that builds a feature representation from raw EEG signals, as well as two modules performing localization and classification respectively. Results and comparison with other methods: The proposed approach is tested on 4 datasets and 3 types of events (spindles, K-complexes, arousals) and compared to the current state-of-the-art detection algorithms. Conclusions: Results demonstrate the versatility of this new approach and improved performance compared to the current state-of-the-art detection methods.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| K-complex detection | MASS SS2 | DOSED | F1-score (@IoU = 0.3) | 0.6 | #3 of 4 | Archive leaderboard | report |
| Sleep Arousal Detection | MESA | DOSED (3 EEG + 2 EOG) | F1-score (@IoU = 0.3) | 0.71 | #1 of 3 | Archive leaderboard | report |
| Sleep Arousal Detection | MESA | DOSED (1 EEG) | F1-score (@IoU = 0.3) | 0.61 | #2 of 3 | Archive leaderboard | report |
| Sleep apnea detection | Dreem_NCT03657329 | DOSED | Accuracy | 81% | #1 of 1 | Archive leaderboard | report |
| Sleep apnea detection | Dreem_NCT03657329 | DOSED | F1-score (@IoU = 0.3) | 0.57 ± 0.23 | #1 of 1 | Archive leaderboard | report |
| Sleep apnea detection | Dreem_NCT03657329 | DOSED | Mean AHI Error | 4.69 ± 4.25 | #1 of 1 | Archive leaderboard | report |
| Spindle Detection | MASS SS2 | DOSED | F1-score (@IoU = 0.3) | 0.75 | #3 of 6 | Archive leaderboard | report |
| Spindle Detection | Stanford Sleep Cohort (SSC) | DOSED | F1-score (@IoU = 0.3) | 0.48 | #1 of 1 | Archive leaderboard | report |
| Spindle Detection | Wisconsin Sleep Cohort (WSC) | DOSED | F1-score (@IoU = 0.3) | 0.46 | #1 of 1 | 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
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