Objectives: Rapid Eye Movement Sleep Behaviour Disorder (RBD) is regarded as a prodrome of neurodegeneration, with a high conversion rate to $\alpha$–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.

Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson’s Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or cognitive task within an interactive videogame in a virtual environment. These strategies may widely benefit from being tailored to the patient’s needs and engagement patterns. In this pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measure the user’s engagement during an exergame. As a first step, healthy subjects were recruited to assess the system’s capability to distinguish between (1) rest and gaming conditions and (2) gaming at different complexity levels, through Machine Learning supervised models. Both EEG and eye-blink features were employed. The results indicate the ability of the exergame to stimulate engagement and the capability of the supervised classification models to distinguish resting stage from game-play (accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified, which could help define models of engagement trends. This result is a starting point in developing an effectively subject-tailored exergaming system.

Rapid‐eye movement (REM) sleep, or paradoxical sleep, accounts for 20–25% of total night‐time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and related activities would help to improve their quality of life. There is a need to accurately classify REM and the other stages of sleep in order to properly identify and monitor parasomnias. This study proposes a method for the identification of REM sleep from raw single‐channel electroencephalogram data, employing novel features based on REM microstructures. Sleep stage classification was performed by means of random forest (RF) classifier, K‐nearest neighbour (K‐NN) classifier and random Under sampling boosted trees (RUSBoost); the classifiers were trained using a set of published and novel features. REM detection accuracy ranges from 89% to 92.7%, and the classifiers achieved a F‐1 score (REM class) of about 0.83 (RF), 0.80 (K‐NN), and 0.70 (RUSBoost). These methods provide encouraging outcomes in automatic sleep scoring and REM detection based on raw single‐channel electroencephalogram, assessing the feasibility of a home sleep monitoring device with fewer channels.

Voice is a reservoir of valuable health data. Recent studies highlighted its efficacy in predicting sleep quality, and its potential as biomarker of neurodegeneration. This study assesses the feasibility of a Telemedicine system for the evaluation of sleep quality through brief vocal recordings. Machine Learning models were employed in the binary classification between good and poor sleepers, with great performance in scoring poor sleep quality – 88% and 85% F-1 score on a 5-fold Cross Validation (CV) for females and males, respectively. Moreover, the correlation between perceived sleep quality and a validated global score was studied, as well as the influence of external factors and sleep-wake schedule.