Assessing REM Sleep Behaviour Disorder: From Machine Learning Classification to the Definition of a Continuous Dissociation Index

Irene Rechichi, Antonella Iadarola, Maurizio Zibetti, Alessandro Cicolin and Gabriella Olmo
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Irene Rechichi: Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Antonella Iadarola: Sleep Disorders Center, Department of Neuroscience, University of Turin, 10126 Turin, Italy
Maurizio Zibetti: Department of Neuroscience, University of Turin, 10126 Turin, Italy
Alessandro Cicolin: Sleep Disorders Center, Department of Neuroscience, University of Turin, 10126 Turin, Italy
Gabriella Olmo: Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy

IJERPH, 2021, vol. 19, issue 1, 1-18

Abstract: Objectives: Rapid Eye Movement Sleep Behaviour Disorder (RBD) is regarded as a prodrome of neurodegeneration, with a high conversion rate to α –synucleinopathies such as Parkinson’s Disease (PD). The clinical diagnosis of RBD co–exists with evidence of REM Sleep Without Atonia (RSWA), a parasomnia that features loss of physiological muscular atonia during REM sleep. The objectives of this study are to implement an automatic detection of RSWA from polysomnographic traces, and to propose a continuous index (the Dissociation Index) to assess the level of dissociation between REM sleep stage and atonia. This is performed using Euclidean distance in proper vector spaces. Each subject is assigned a dissociation degree based on their distance from a reference, encompassing healthy subjects and clinically diagnosed RBD patients at the two extremes. Methods: Machine Learning models were employed to perform automatic identification of patients with RSWA through clinical polysomnographic scores, together with variables derived from electromyography. Proper distance metrics are proposed and tested to achieve a dissociation measure. Results: The method proved efficient in classifying RSWA vs. not-RSWA subjects, achieving an overall accuracy, sensitivity and precision of 87%, 93% and 87.5%, respectively. On its part, the Dissociation Index proved to be promising in measuring the impairment level of patients. Conclusions: The proposed method moves a step forward in the direction of automatically identifying REM sleep disorders and evaluating the impairment degree. We believe that this index may be correlated with the patients’ neurodegeneration process; this assumption will undergo a robust clinical validation process involving healthy, RSWA, RBD and PD subjects.

Keywords: machine learning; REM Sleep Behaviour Disorder; electromyography; Parkinson’s disease; RSWA; RBD detection (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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