REM Sleep Behavior Disorder (RBD) is a sleep disorder characterized by harmful motor behaviors occurring in conjunction with vivid dream experiences. Recent research has increasingly recognized RBD as an early manifestation of neurodegenerative conditions such as Parkinson’s Disease, Multiple System Atrophy, and Dementia with Lewy bodies. Diagnosis traditionally relies on clinical history, polysomnography, and manual-visual scoring methods, which are time-consuming and require specialized expertise. This thesis explores the application of automated algorithms, particularly the REM Sleep Atonia Index (RAI), and Dissociation Index (DI) to identify and evaluate RBD. RBD is characterized by a loss of muscle atonia during REM sleep, known as REM Sleep Without Atonia (RSWA).