Abstract
Parkinson’s disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients’ quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.
Highlights
Parkinson’s disease (PD) is a common degenerative disorder of the central nervous system characterized by both motor and non-motor symptoms
The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). This predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time
Supervised machine learning techniques, such as support vector regression (SVR),17 random forest (RF),3 adaptive neuro-fuzzy inference system (ANFIS),12,17 and linear regression (LR)11 were tested in this work
Summary
Parkinson’s disease (PD) is a common degenerative disorder of the central nervous system characterized by both motor and non-motor symptoms. In clinical practice, PD motor signs are assessed by neurologists while they observe patients performing motor tasks described in section III of the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS III) and patient diaries.. The clinical evaluation is mainly based on the experience of clinicians that assign a score ranging from 0 (no signs clinically evident) to 4 (severely impaired) for each task performed by patients. To evaluate the PD progression, it is necessary to understand the long-term monitoring of the disease, assessing the patients periodically. This traditional evaluation, based on clinical scales, relies on clinical expertise, and is subjected to inter- and intra- observer variability.. The traditional evaluation methods are suboptimal for PD diagnosis and monitoring, and novel methods and technologies should be investigated.
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