Work

A Kinematic Data-Driven Approach to Differentiate Involuntary Choreic Movements in Individuals With Neurological Conditions

Year: 2022

Type: article

Abstract: OBJECTIVE: The ability to differentiate similar choreic involuntary movements could lay the groundwork for the development of a minimally-invasive screening tool for their etiology and provide in-depth understandings of pathophysiology. As a first step, we investigate kinematic differences between Huntington's disease (HD) chorea and Parkinson's disease (PD) choreic levodopa-induced dyskinesia (LID), which have distinct pathological causes yet share a great kinematic resemblance. METHODS: Twenty subjects with HD and ten subjects with PD stood with both upper limbs in front of them for approximately 60 seconds. The three-dimensional velocity time-series of involuntary movements of both hands were segmented into one-dimensional sub-movements abutted by velocity zero-crossings. A combination of unsupervised and supervised machine learning algorithms was employed to automatically select data features extracted from sub-movements and distinguish the two types of involuntary choreic movements. RESULTS: The trained model was able to accurately classify chorea vs. LID with an Area Under the Receiver Operating Characteristic Curve of 99.5%. A set of important features contributing to the construction of the classification model were identified and investigated. CONCLUSION: The trained model may serve as a tool for the automatic identification of different types of involuntary choreic movements, enabling continuous monitoring and personalized treatment for patients in various clinical settings. SIGNIFICANCE: The results provide insights into kinematic characteristics of HD chorea and PD LID, which is the first step towards an improved general understanding of involuntary choreic movements.

Source: IEEE Transactions on Biomedical Engineering

DOI: 10.1109/tbme.2022.3177396

Authors: Yunda Liu, Brandon Oubre, Christian Duval, Sunghoon Ivan Lee, Jean-Francois Daneault

Institutions: University of Massachusetts System, Université du Québec à Montréal, University of Massachusetts Amherst, Rutgers, The State University of New Jersey

Language: English

Topic: Neurological disorders and treatments

Subfield: Neurology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Neurological Disorders and Stroke