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Motion sensor-based assessment of Parkinson’s disease motor symptoms during leg agility tests: results from levodopa challenge
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0002-1548-5077
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2019 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208Article in journal (Refereed) Epub ahead of print
Abstract [en]

Parkinson’s 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 10-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.

Place, publisher, year, edition, pages
IEEE, 2019.
National Category
Physiotherapy Other Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-29542DOI: 10.1109/JBHI.2019.2898332OAI: oai:DiVA.org:du-29542DiVA, id: diva2:1290650
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-02-27Bibliographically approved
In thesis
1. Multisensor data-driven methods for automated quantification of motor symptoms in Parkinson’s disease
Open this publication in new window or tab >>Multisensor data-driven methods for automated quantification of motor symptoms in Parkinson’s disease
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The overall aim of this thesis was to develop and evaluate new data-driven methods for supporting treatment and providing information for better management of Parkinson’s disease (PD).

This disease is complex and progressive. There is a large amount of inter- and intra-variability in motor symptoms of patients with PD (PwPD). Current evaluation of motor symptoms which is done at clinics by using clinical rating scales provides limited and only part of the health status of PwPD. PD requires an accurate assessment that is approved by clinics. Therefore frequent evaluation of symptoms at micro-level is required.

Sensor systems including smartphone and motion sensors were employed to collect data from PwPD and the recruited healthy controls. Repeated measures consisting of subjective assessment of symptoms and objective assessment of motor functions were collected.

First, the smartphone-based data-driven methods were developed to quantify the dexterity presented in fine motor tests of spiral drawing and alternate tapping. The upper extremities temporal irregularity measure presented in spiral drawing tests of PwPD was further analyzed by the approximate entropy (ApEn) method. Second, tri-axial motion sensor data were collected from various tests like leg agility, walking, and rapid alternating movements of hands of PwPD during a full cycled levodopa challenge. Data driven methods for quantification of leg agility tests and a combination of multiple motor tests were developed. The clinimetric properties of the methods such as reliability, validity, and responsiveness were evaluated. In addition, the feasibility of using smartphone inertial measurement unit (IMU) sensors in comparison to motion sensors for quantifying the motor states in PD during rapid alternating movements of hands tests was investigated.

Results of the developed methods for quantification of PD motor symptoms via dexterity tests in a smartphone can be used for measuring treatment related changes in PwPD. Investigation of the ApEn method showed good sensitivity and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity at micro-level. High convergence validity resulted from using motion sensors during leg agility tests which led to valid and reliable objective measures of PD motor symptoms. The results of fusion of sensor data gathered during standardized motor tests were promising and led to highly valid, reliable and sensitive objective measures of PD motor symptoms. The results of the analyzing acceleration IMU data showed that smartphone IMU is capable of capturing symptom information from hand rotation tests. It can provide sufficient data for quantification of the motor states.

The findings from the data-driven methodology in this thesis can be used in development of systems for follow up of the effects of treatment and individualizing treatments in PD.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2019
Series
Dalarna Doctoral Dissertations ; 10
Keywords
Parkinson’s disease, motor symptoms, motion sensors, smartphone, microdata, multivariate analysis, data-driven, support vector machine, stepwise regression, predictive models
National Category
Medical Laboratory and Measurements Technologies Biomedical Laboratory Science/Technology Computer Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29583 (URN)978-91-88679-00-0 (ISBN)
Public defence
2019-04-26, Clas Ohlson, Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2019-03-28 Created: 2019-02-27 Last updated: 2019-03-28Bibliographically approved

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Aghanavesi, Somayeh

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