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Motion Sensor-Based Assessment of Parkinson's Disease Motor Symptoms During Leg Agility Tests: Results From Levodopa Challenge

Artikel i vetenskaplig tidskrift
Författare S. Aghanavesi
Filip Bergquist
D. Nyholm
M. Senek
M. Memedi
Publicerad i Ieee Journal of Biomedical and Health Informatics
Volym 24
Nummer/häfte 1
Sidor 111-119
ISSN 2168-2194
Publiceringsår 2020
Publicerad vid Institutionen för neurovetenskap och fysiologi, sektionen för farmakologi
Sidor 111-119
Språk en
Länkar dx.doi.org/10.1109/jbhi.2019.289833...
Ämnesord Legged locomotion, Diseases, Foot, Feature extraction, Machine learning, Standards, Acceleration, Leg agility, Parkinson's disease, support, vector machine, stepwise regression, predictive models, society-sponsored revision, scale mds-updrs, dyskinesia assessment, stepwise regression, movement, quantification, impairment, Computer Science, Mathematical & Computational Biology, Medical, Informatics
Ämneskategorier Klinisk medicin

Sammanfattning

Parkinsons disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the unified PD rating scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees, and linear regression, using ten-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS 31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: 26-leg agility, 27-arising from chair, and 29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

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