Papers › CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
Konstantinos Kontras, Christos Chatzichristos, Huy Phan, Johan Suykens, Maarten De Vos
Sleep abnormalities can have severe health consequences. Automated sleep staging, i.e. labelling the sequence of sleep stages from the patient's physiological recordings, could simplify the diagnostic process. Previous work on automated sleep staging has achieved great results, mainly relying on the EEG signal. However, often multiple sources of information are available beyond EEG. This can be particularly beneficial when the EEG recordings are noisy or even missing completely. In this paper, we propose CoRe-Sleep, a Coordinated Representation multimodal fusion network that is particularly focused on improving the robustness of signal analysis on imperfect data. We demonstrate how appropriately handling multimodal information can be the key to achieving such robustness. CoRe-Sleep tolerates noisy or missing modalities segments, allowing training on incomplete data. Additionally, it shows state-of-the-art performance when testing on both multimodal and unimodal data using a single model on SHHS-1, the largest publicly available study that includes sleep stage labels. The results indicate that training the model on multimodal data does positively influence performance when tested on unimodal data. This work aims at bridging the gap between automated analysis tools and their clinical utility.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sleep Stage Detection | SHHS | CoRe-Sleep (EEG-EOG) | Accuracy | 89.5% | #2 of 10 | Archive leaderboard | report |
| Sleep Stage Detection | SHHS | CoRe-Sleep (EEG-EOG) | Cohen's Kappa | 0.853 | #2 of 10 | Archive leaderboard | report |
| Sleep Stage Detection | SHHS | CoRe-Sleep (EEG-EOG) | Macro-F1 | 0.823 | #2 of 10 | 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.
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