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This study compared automated sleep staging models for diagnosing REM sleep behaviour disorder (RBD), finding that single-channel EEG models trained on healthy individuals perform poorly when applied to patient populations. A multimodal approach incorporating EEG, EOG, EMG, and ECG signals significantly improved REM sleep detection accuracy in RBD patients, with analysis revealing that EOG (eye movement) signals are particularly important for this population. The optimal configuration used four EEG channels plus EOG, achieving substantially better performance than minimal-sensor approaches while reducing variability across datasets.
Why it matters
Accurate REM sleep staging is essential for diagnosing RBD, an important early marker of neurodegenerative diseases like Parkinson's. This research demonstrates that automated sleep staging systems must be trained on diverse patient populations and incorporate multiple signal types to work reliably in clinical settings, rather than relying on simpler systems developed using only healthy subjects.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background: Accurate REM identification is critical for diagnosing REM sleep behaviour disorder (RBD), yet many automated sleep staging systems, especially single-channel EEG models trained on healthy cohorts, do not generalise well to real-life polysomnography (PSG) performed in patients. Objective: To compare a feature-based Random Forest (RF) model tuned for RBD with a state-of-the-art single-EEG deep architecture (AttnSleep), and to assess the impact of cohort adaptation and multimodal inputs (EEG, EOG, EMG, ECG). Methods: Experiments used 89 multi-site in-clinic PSGs (SleepWearables Phase-1) plus 53 MASS healthy controls (mean age 63, std 5 years), with 10-fold cross-validation and out-of-fold evaluation. Model performance was assessed using Cohen’s kappa, and attention-based modality analysis was performed to quantify signal contributions. Results: When applied out-of-the-box after training on open-source healthy datasets, both models achieved moderate agreement overall (Cohen’s kappa = 0.46), but performance declined in RBD, particularly for REM sleep (AttnSleep Cohen’s kappa = 0.19 vs RF Cohen’s kappa = 0.44), highlighting limited cross-cohort generalisation. The multimodal model improved overall agreement (Cohen’s kappa 0.59 – 0.60) and performance in RBD (Cohen’s kappa 0.45 – 0.46), with gains most pronounced in REM (Cohen’s kappa 0.45 – 0.49). Attention-based modality analysis identified EEG as the dominant signal, increased EOG contribution during REM, and elevated ECG importance during N3. In RBD subjects, EOG weighting increased relative to non-RBD controls (Delta = +0.081). Guided by these weights, a reduced four-channel EEG model matched full multimodal performance in non-RBD subjects, and adding EOG achieved the best overall configuration (Cohen’s kappa = 0.61 overall; Cohen’s kappa = 0.48 in RBD) with improved REM classification (53% vs 45% recall). Inclusion of EOG also reduced inter-dataset variability in REM staging. Nonetheless, staging performance in RBD remained lower than in controls, particularly for REM. Conclusions: These results highlight the limited generalisability of minimal-sensor models trained on healthy cohorts, the value of mixed cohort-specific training, and the benefit of multimodal integration and attention-guided channel selection, rather than minimal-sensor approaches alone, for robust clinical sleep staging in pathological populations such as RBD.
Source: Multi-modal sleep staging in the clinic for REM sleep behaviour disorder