Prediction of motor Unified Parkinson's Disease Rating Scale scores in patients with Parkinson's disease using surface electromyography

Abstract

Objective: Parkinson's disease (PD) is a chronic neurodegenerative disorder with increasing prevalence in the elderly. Especially patients with advanced PD often require complex medication regimens due to fluctuations, that is abrupt transitions from ON to OFF or vice versa. Current gold standard to quantify PD-patients' motor symptoms is the assessment of the Unified Parkinson's Disease Rating Scale (UPDRS), which, however, is cumbersome and may depend upon investigators. This work aimed at developing a mobile, objective and unobtrusive measurement of motor symptoms in PD.

Methods: Data from 45 PD-patients was recorded using surface electromyography (sEMG) electrodes attached to a wristband. The motor paradigm consisted of a tapping task performed with and without dopaminergic medication. Our aim was to predict UPDRS scores from the sEMG characteristics with distinct regression models and machine learning techniques.

Results: A random forest regression model outnumbered other regression models resulting in a correlation of 0.739 between true and predicted UPDRS values.

Conclusions: PD-patients' motor affection can be extrapolated from sEMG data during a simple tapping task. In the future, such records could help determine the need for medication changes in telemedicine applications.

Significance: Our findings support the utility of wearables to detect Parkinson's symptoms and could help in developing tailored therapies in the future.

Keywords: Electromyography; Machine learning; Parkinson’s disease; Regression; UPDRS; Wearables.