Sleep Disorders have received much attention in recent years, as they are related to the risk and pathogenesis of neurodegenerative diseases. Notably, REM Sleep Behaviour Disorder (RBD) is considered an early symptom of -synucleinopathies, with a conversion rate to Parkinson’s Disease (PD) up to 90%. Recent studies also highlighted the role of disturbed Non-REM Slow Wave Sleep (SWS) in neurodegenerative diseases pathogenesis and its link to cognitive outcomes in PD and Dementia. However, the diagnosis of sleep disorders is a long and cumbersome process. This study proposes a method for automatically detecting RBD from single-channel EEG data, by analysing segments recorded during both REM sleep and SWS. This paper inspects the underlying microstructure of the two stages and includes a comparison of their performance to discuss their potential as markers for RBD. Machine Learning models were employed in the binary classification between healthy and RBD subjects, with an 86% averaged accuracy on a 5-fold cross-validation when considering both stages. Besides, SWS features alone proved promising in detecting RBD, scoring a 91% sensitivity (RBD class). These findings suggest the applicability of an EEG-based, low-cost, automatic detection of RBD, leading to potential use in the early diagnosis of neurodegeneration, thus allowing for disease-modifying interventions.
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