Umberto Mosca
Automatic classification of REM Sleep Behaviour Disorder exploiting Heart Rate Variability and Electromyography.
Rel. Gabriella Olmo, Irene Rechichi. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2023
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Abstract
Recent works highlighted that one of the fundamental roles of sleep is the removal of toxic metabolites produced during wakefulness. Consequently, a correlation was found between the quality of both rapid-eye movement (REM) sleep and deep sleep (i.e., slow wave sleep, SWS) and the development of neurodegenerative diseases. In this regard, the study of RBD has assumed great importance as it is considered a symptom that precedes, even by decades, the development of α-synucleinopathies, and a conversion rate to Parkinson's disease of around 90% has been found. The diagnosis of REM sleep behaviour disorder (RBD) is a lengthy and cumbersome process involving the analysis of polysomnography (approximately 8 hours of recording on more than 10 tracings) by a sleep expert.
Consequently, the aim of this study is to develop a model capable of easing the physician's workload by performing an automatic binary classification between healthy and RBD patients
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