Identification of patients with REM sleep behavior disorder with a small and portable depth sensor

Patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) are considered to be in the prodromal stage of alpha-synucleinopathy. They exhibit abnormal muscle activity during REM sleep and dream enactment. Currently, diagnosing iRBD requires an in-lab video-polysomnography (v-PSG), which involves manual and time-consuming analyses. In this study, we explore the feasibility of using a small and portable depth sensor to identify patients with iRBD, potentially enabling home-based assessments. Our study included 10 patients with iRBD, 10 patients with differential diagnoses of RBD, and 5 control subjects. Depth data were recorded simultaneously with v-PSG. After preprocessing to remove noise, we tested various temporal, spatiotemporal convolutional kernels, and dense optical flow to generate motion maps from depth data. Movements were then automatically detected during REM and non-REM sleep, and relevant features were extracted. Logistic regression models with leave-one-subject-out cross-validation were used to discriminate patients with iRBD, with performance evaluated based on the area under the curve (AUC). The highest AUC (0.900) was achieved when analyzing REM and non-REM sleep movements, using a convolutional kernel to detect fast movements. High performance (AUC = 0.893) was also observed when using features from movements in REM sleep identified with a spatiotemporal kernel. Although not achieving as high performance as when using movements annotated by experts (AUC of 0.993), our findings support the feasibility of using a small, portable depth sensor for the automatic detection of patient movements and the reliable identification of patients with iRBD.Clinical relevance- The proposed approach has the potential to be implemented in home environments to detect patients with iRBD, enabling faster and more objective identification.