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Sleep abnormalities can have severe health consequences. Automated sleepstaging, i.e. labelling the sequence of sleep stages from the patient'sphysiological recordings, could simplify the diagnostic process. Previous workon automated sleep staging has achieved great results, mainly relying on theEEG signal. However, often multiple sources of information are available beyondEEG. This can be particularly beneficial when the EEG recordings are noisy oreven missing completely. In this paper, we propose CoRe-Sleep, a CoordinatedRepresentation multimodal fusion network that is particularly focused onimproving the robustness of signal analysis on imperfect data. We demonstratehow appropriately handling multimodal information can be the key to achievingsuch robustness. CoRe-Sleep tolerates noisy or missing modalities segments,allowing training on incomplete data. Additionally, it shows state-of-the-artperformance when testing on both multimodal and unimodal data using a singlemodel on SHHS-1, the largest publicly available study that includes sleep stagelabels. The results indicate that training the model on multimodal data doespositively influence performance when tested on unimodal data. This work aimsat bridging the gap between automated analysis tools and their clinicalutility.

CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities