Actigraphy Enables Home Screening of Rapid Eye Movement Behavior Disorder in Parkinson's Disease.

OBJECTIVES: Rapid eye movement sleep behavior disorder (RBD) is a potentially harmful, often overlooked sleep disorder affecting up to 70% of Parkinson's disease patients. Current diagnosis relies on nocturnal video-polysomnography, which is an expensive and cumbersome examination requiring specific clinical expertise. Here, we explored the use of wrist actigraphy to enable automatic RBD diagnoses in home settings. METHODS: A total of 26 Parkinson's disease patients underwent 2-week home wrist actigraphy, followed by two in-laboratory evaluations. Patients were classified as RBD versus non-RBD based on dream enactment history and video-polysomnography. We comprehensively characterized patients' movement patterns during sleep using actigraphic signals. We then trained machine learning classification algorithms to discriminate patients with or without RBD using the most relevant features. Classification performance was quantified with respect to clinical diagnosis, separately for in-laboratory and at-home recordings. Performance was further validated in a control group of non-Parkinson's disease patients with other sleep conditions. RESULTS: To characterize RBD, actigraphic features extracted from both (1) individual movement episodes and (2) global nocturnal activity were critical. RBD patients were more active overall, and showed movements that were shorter, of higher magnitude, and more scattered in time. Using these features, our classification algorithms reached an accuracy of 92.9 ± 8.16% during in-clinic tests. When validated on home recordings in Parkinson's disease patients, accuracy reached 100% over a 2-week window, and was 94.4% in non-Parkinson's disease control patients. Features showed robustness across tests and conditions. INTERPRETATION: These results open new perspectives for faster, cheaper, and more regular screening of sleep disorders, both for routine clinical practice and clinical trials. ANN NEUROL 2023;93:317-329.

Principles of gait encoding in the subthalamic nucleus of people with Parkinson's disease.

Disruption of subthalamic nucleus dynamics in Parkinson's disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson's disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson's disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson's disease.

Personalized Sleep Spindle Detection in Whole Night Polysomnography.

The present study proposes a new personalized sleep spindle detection algorithm, suggesting the importance of an individualized approach. We identify an optimal set of features that characterize the spindle and exploit a support vector machine to distinguish between spindle and nonspindle patterns. The algorithm is assessed on the open source DREAMS database, that contains only selected part of the polysomnography, and on whole night polysomnography recordings from the SPASH database. We show that on the former database the personalization can boost sensitivity, from 84.2% to 89.8%, with a slight increase in specificity, from 97.6% to 98.1%. On a whole night polysomnography instead, the algorithm reaches a sensitivity of 98.6% and a specificity of 98.1%, thanks to the personalization approach. Future work will address the integration of the spindle detection algorithm within a sleep scoring automated procedure.