Rapid Eye Movement (REM) Behavior Disorder (RBD) is a parasomnia strongly associated with future neurodegenerative diseases, yet its diagnosis depends on labor-intensive polysomnography (PSG) analysis and analytical indices such as the REM Atonia Index (RAI). Automated detection is challenging due to the temporal sparsity and variability of RBD-related events.

This work presents SOMNUS-RBD, a hierarchical deep learning framework for patient-level RBD prediction from REM sleep PSG. REM segments are encoded into embeddings, then processed with channel-level attention pooling and temporal LogSumExp aggregation to capture sparse pathological activations. Five data configurations were evaluated on a 49-patient cohort using 10-repeated 5-fold cross-validation: (1) chin electromyography (EMG) embeddings alone, (2) multichannel EMG with independent per-channel embeddings, (3) multichannel EMG with joint multi-channel embeddings, (4) multimodal EMG, electroencephalography (EEG) and electrooculography (EOG) with independent per-channel embeddings, and (5) multimodal EMG, EEG, and EOG with unified per-modality embeddings.

Using only chin EMG, the proposed framework showed improvements compared to the analytical baseline (RAI), as accuracy increased from 0.735 to 0.786 and recall from 0.577 to 0.781, while maintaining competitive precision and AUC. Independent multichannel EMG achieved the best performance with an AUC of 0.832 and a recall of 0.827. In contrast, grouping channels prior to embedding extraction reduced discriminative performance. Attention weights highlighted EMG as the dominant modality compared to EEG and EOG, and temporal importance scores aligned with physiologically meaningful segments of loss of atonia. These findings suggest that SOMNUS-RBD surpasses traditional analytical indices while providing intrinsic channel- and timestep-relevant measures for RBD detection.