Validation of automated detection of REM sleep without atonia using in-laboratory and in-home recordings.

Study objectives: To evaluate the concordance between visual scoring and automated detection of REM sleep without atonia (RSWA) and the validity and reliability of in-home automated-RSWA detection in REM sleep behavior disorder (RBD) patients and a control group (CG).

Methods: Sleep Profiler signals were acquired during simultaneous in-laboratory polysomnography in 24 isolated RBD patients. Chin and arm RSWA measures visually scored by an expert sleep technologist were compared to algorithms designed to automate RSWA detection. In a second cohort, the accuracy of automated-RSWA detection for discriminating between RBD and CG (n = 21 and 42, respectively) was assessed in multi-night in-home recordings.

Results: For the in-laboratory studies, agreement between visual and auto-scored RSWA from the chin and arm were excellent, with intra-class correlations of 0.89 and 0.95, respectively, and substantial, based on Kappa scores of 0.68 and 0.74, respectively. For classification of iRBD patients versus controls, specificities derived from auto-detected RSWA densities obtained from in-home recordings were 0.88 for the chin, 0.93 for the arm, and 0.90 for the chin or arm, while the sensitivities were 0.81, 0.81 and 0.86, respectively. The night-to-night consistencies of the respective auto-detected RSWA densities were good based on intra-class correlations of 0.81, 0.79 and 0.84, however some night-to-night disagreements in abnormal RSWA detection were observed.

Conclusions: When compared to expert visual RSWA scoring, automated RSWA detection demonstrates promise for detection of RBD. The night-to-night reliability of chin- and arm-RSWA densities acquired in-home were equivalent.