Interpretable HRV-Based Detection of REM Sleep Behaviour Disorder via Explainable Boosting Machines

Rapid Eye Movement (REM) sleep behaviour disorder (RBD) is an early biomarker of neurodegeneration but requires resource-intensive video-polysomnography (v-PSG) for diagnosis. This study presents an interpretable pipeline for automated detection of idiopathic RBD (iRBD) using heart rate variability (HRV) features extracted from ECG during sleep. HRV metrics are computed separately for each sleep stage to capture stage-specific autonomic signatures. Selected features are used to train an Explainable Boosting Machine (EBM). The approach is evaluated on an age-matched cohort of 67 subjects (33 healthy controls and 34 iRBD) using nested LeaveOne-Subject-Out cross-validation. Across ten random seeds, the REM stage yielded the best performance (AUC = 0.82 ± 0.02, accuracy = 0.75 ± 0.04). Four HRV features are consistently selected across folds, indicating stable autonomic markers of iRBD and highlighting the potential of single-lead ECG as a scalable screening tool.